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https://www.hybrid-analysis.com/sample/255948e335dd9f873d11bf0224f8d180cd097509d23d27506292c22443fa92b8
https://www.facebook.com/PS5Giveaways2021
https://cgvmovie.cookpad-blog.jp/articles/589986
https://myanimelist.net/blog.php?eid=850892
https://comicvine.gamespot.com/profile/full-tv-free/about-me/
https://pantip.com/topic/40658194
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{}
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fullshowbox/full-tv-free
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[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
URL
URL
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URL
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|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
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123movies-watch-online-movie-full-free-2021
https://myanimelist.net/blog.php?eid=849353
https://comicvine.gamespot.com/profile/nacenetwork21/about-me/
https://pantip.com/topic/40639721
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{}
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fullshowbox/nacenetwork21
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2022-03-02T23:29:05+00:00
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[] |
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TAGS
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123movies-watch-online-movie-full-free-2021
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[] |
[
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{}
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fullshowbox/networkprofile
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
URL
URL
URL
URL
URL
URL
URL
URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null | null |
https://ragbrai.com/groups/hd-movie-watch-french-exit-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-nobody-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-voyagers-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-godzilla-vs-kong-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-raya-and-the-last-dragon-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-mortal-kombat-2021-full-movie-online-for-free/
https://ragbrai.com/groups/hd-movie-watch-the-father-2021-full-movie-online-for-free/
|
{}
| null |
fullshowbox/ragbrai
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
URL
URL
URL
URL
URL
URL
URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
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[
"passage: TAGS\n#region-us \n"
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null | null |
transformers
|
# Funnel Transformer intermediate model (B6-6-6 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
**Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the `intermediate` model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate-base")
model = FunnelBaseModel.from_pretrained("funnel-transformer/intermediate-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate-base")
model = TFFunnelBaseModel.from_pretrained("funnel-transformer/intermediate-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/intermediate-base
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer intermediate model (B6-6-6 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the 'intermediate' model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer intermediate model (B6-6-6 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'intermediate' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer intermediate model (B6-6-6 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'intermediate' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
72,
103,
289,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer intermediate model (B6-6-6 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'intermediate' model in that case."
] |
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] |
null | null |
transformers
|
# Funnel Transformer intermediate model (B6-6-6 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate")
model = FunneModel.from_pretrained("funnel-transformer/intermediate")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate")
model = TFFunnelModel.from_pretrained("funnel-transformer/intermediatesmall")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/intermediate
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer intermediate model (B6-6-6 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer intermediate model (B6-6-6 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer intermediate model (B6-6-6 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
77,
103,
206,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer intermediate model (B6-6-6 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs."
] |
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null | null |
transformers
|
# Funnel Transformer large model (B8-8-8 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
**Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the `large` model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base")
model = FunnelBaseModel.from_pretrained("funnel-transformer/large-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base")
model = TFFunnelBaseModel.from_pretrained("funnel-transformer/large-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/large-base
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer large model (B8-8-8 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the 'large' model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer large model (B8-8-8 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'large' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer large model (B8-8-8 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'large' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
72,
102,
288,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer large model (B8-8-8 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'large' model in that case."
] |
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null | null |
transformers
|
# Funnel Transformer large model (B8-8-8 with decoder)
Pretrained model on English language using a similar objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large")
model = FunneModel.from_pretrained("funnel-transformer/large")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large")
model = TFFunnelModel.from_pretrained("funnel-transformer/large")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/large
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer large model (B8-8-8 with decoder)
Pretrained model on English language using a similar objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer large model (B8-8-8 with decoder)\n\nPretrained model on English language using a similar objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer large model (B8-8-8 with decoder)\n\nPretrained model on English language using a similar objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
77,
101,
206,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer large model (B8-8-8 with decoder)\n\nPretrained model on English language using a similar objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs."
] |
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] |
null | null |
transformers
|
# Funnel Transformer medium model (B6-3x2-3x2 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
**Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the `medium` model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium-base")
model = FunnelBaseModel.from_pretrained("funnel-transformer/medium-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium-base")
model = TFFunnelBaseModel.from_pretrained("funnel-transformer/medium-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/medium-base
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer medium model (B6-3x2-3x2 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the 'medium' model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer medium model (B6-3x2-3x2 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'medium' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer medium model (B6-3x2-3x2 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'medium' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
72,
105,
288,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer medium model (B6-3x2-3x2 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'medium' model in that case."
] |
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null | null |
transformers
|
# Funnel Transformer medium model (B6-3x2-3x2 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium")
model = FunneModel.from_pretrained("funnel-transformer/medium")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium")
model = TFFunnelModel.from_pretrained("funnel-transformer/medium")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/medium
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer medium model (B6-3x2-3x2 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer medium model (B6-3x2-3x2 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer medium model (B6-3x2-3x2 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
72,
105,
206,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer medium model (B6-3x2-3x2 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs."
] |
[
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0.11025729775428772,
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null | null |
transformers
|
# Funnel Transformer small model (B4-4-4 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
**Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the `small` model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small-base")
model = FunnelBaseModel.from_pretrained("funnel-transformer/small-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small-base")
model = TFFunnelBaseModel.from_pretrained("funnel-transformer/small-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/small-base
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer small model (B4-4-4 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the 'small' model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer small model (B4-4-4 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'small' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer small model (B4-4-4 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'small' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
77,
102,
288,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer small model (B4-4-4 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'small' model in that case."
] |
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null | null |
transformers
|
# Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small")
model = FunneModel.from_pretrained("funnel-transformer/small")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small")
model = TFFunnelModel.from_pretrained("funnel-transformer/small")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/small
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer small model (B4-4-4 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Funnel Transformer small model (B4-4-4 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
76,
102,
206,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# Funnel Transformer small model (B4-4-4 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs."
] |
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null | null |
transformers
|
# Funnel Transformer xlarge model (B10-10-10 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
**Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the `xlarge` model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge-base")
model = FunnelBaseModel.from_pretrained("funnel-transformer/xlarge-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelBaseModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge-base")
model = TFFunnelBaseModel.from_pretrained("funnel-transformer/xlarge-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/xlarge-base
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us
|
# Funnel Transformer xlarge model (B10-10-10 without decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth
of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if
you need one input per initial token. You should use the 'xlarge' model in that case.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer xlarge model (B10-10-10 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'xlarge' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Funnel Transformer xlarge model (B10-10-10 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'xlarge' model in that case.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
77,
104,
289,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #region-us \n# Funnel Transformer xlarge model (B10-10-10 without decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nNote: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth\nof the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if\nyou need one input per initial token. You should use the 'xlarge' model in that case."
] |
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null | null |
transformers
|
# Funnel Transformer xlarge model (B10-10-10 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge")
model = FunneModel.from_pretrained("funnel-transformer/xlarge")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge")
model = TFFunnelModel.from_pretrained("funnel-transformer/xlarge")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "gigaword"]}
|
feature-extraction
|
funnel-transformer/xlarge
|
[
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.03236"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Funnel Transformer xlarge model (B10-10-10 with decoder)
Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
## Training data
The BERT model was pretrained on:
- BookCorpus, a dataset consisting of 11,038 unpublished books,
- English Wikipedia (excluding lists, tables and headers),
- Clue Web, a dataset of 733,019,372 English web pages,
- GigaWord, an archive of newswire text data,
- Common Crawl, a dataset of raw web pages.
### BibTeX entry and citation info
|
[
"# Funnel Transformer xlarge model (B10-10-10 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Funnel Transformer xlarge model (B10-10-10 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.",
"## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.",
"## Intended uses & limitations\n\nYou can use the raw model to extract a vector representation of a given text, but it's mostly intended to\nbe fine-tuned on a downstream task. See the model hub to look for\nfine-tuned versions on a task that interests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.",
"### How to use\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:",
"## Training data\n\nThe BERT model was pretrained on:\n- BookCorpus, a dataset consisting of 11,038 unpublished books,\n- English Wikipedia (excluding lists, tables and headers),\n- Clue Web, a dataset of 733,019,372 English web pages,\n- GigaWord, an archive of newswire text data,\n- Common Crawl, a dataset of raw web pages.",
"### BibTeX entry and citation info"
] |
[
76,
104,
206,
133,
32,
95,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #en #dataset-bookcorpus #dataset-wikipedia #dataset-gigaword #arxiv-2006.03236 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# Funnel Transformer xlarge model (B10-10-10 with decoder)\n\nPretrained model on English language using a similar objective objective as ELECTRA. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.\n\nDisclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been\nwritten by the Hugging Face team.## Model description\n\nFunnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and\nthe pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs."
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-bbc-headline
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 167 | 2.2978 | 31.8313 | 10.3824 | 29.6182 | 29.4336 | 10.3153 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-base-finetuned-bbc-headline", "results": []}]}
|
text2text-generation
|
furyhawk/t5-base-finetuned-bbc-headline
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-base-finetuned-bbc-headline
==============================
This model is a fine-tuned version of t5-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
63,
98,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-bbc
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 334 | 0.1500 | 24.5024 | 21.4979 | 24.0227 | 24.0303 | 19.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-base-finetuned-bbc", "results": []}]}
|
text2text-generation
|
furyhawk/t5-base-finetuned-bbc
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-base-finetuned-bbc
=====================
This model is a fine-tuned version of t5-base on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
97,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-bbc-headline
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 167 | 3.6454 | 22.4311 | 5.9878 | 20.118 | 20.482 | 18.9009 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-bbc-headline", "results": []}]}
|
text2text-generation
|
furyhawk/t5-small-finetuned-bbc-headline
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-finetuned-bbc-headline
===============================
This model is a fine-tuned version of t5-small on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
63,
98,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-bbc
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3238
- Rouge1: 21.2266
- Rouge2: 16.0927
- Rougel: 19.6785
- Rougelsum: 19.8849
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.4882 | 1.0 | 1001 | 0.3238 | 21.2266 | 16.0927 | 19.6785 | 19.8849 | 19.0 |
### Framework versions
- Transformers 4.12.0
- Pytorch 1.10.0
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-bbc", "results": []}]}
|
text2text-generation
|
furyhawk/t5-small-finetuned-bbc
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-finetuned-bbc
======================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3238
* Rouge1: 21.2266
* Rouge2: 16.0927
* Rougel: 19.6785
* Rougelsum: 19.8849
* Gen Len: 19.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0
* Pytorch 1.10.0
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0\n* Pytorch 1.10.0\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0\n* Pytorch 1.10.0\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.0\n* Pytorch 1.10.0\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 128 | 2.9003 | 19.4784 | 2.8529 | 14.7786 | 15.0614 | 18.9825 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]}
|
text2text-generation
|
furyhawk/t5-small-finetuned-xsum
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.1
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
69,
98,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0964 | 1.0 | 2346 | 7.0532 |
| 6.9055 | 2.0 | 4692 | 6.8710 |
| 6.8574 | 3.0 | 7038 | 6.8917 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]}
|
fill-mask
|
fznmhmmd/bert-base-cased-wikitext2
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8575
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
55,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8273
- Matthews Correlation: 0.5544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5256 | 1.0 | 535 | 0.5419 | 0.4248 |
| 0.3486 | 2.0 | 1070 | 0.5187 | 0.4999 |
| 0.2406 | 3.0 | 1605 | 0.6580 | 0.5054 |
| 0.1692 | 4.0 | 2140 | 0.7455 | 0.5403 |
| 0.1343 | 5.0 | 2675 | 0.8273 | 0.5544 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5543972545286807, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
fznmhmmd/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8273
* Matthews Correlation: 0.5544
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
67,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5571 | 1.0 | 2249 | 6.4684 |
| 6.1921 | 2.0 | 4498 | 6.1984 |
| 6.0016 | 3.0 | 6747 | 6.1112 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]}
|
text-generation
|
fznmhmmd/gpt2-wikitext2
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1112
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
63,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-es-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1788
- Wer: 1.0239
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 0.02 | 100 | 6.6465 | 1.0 |
| No log | 0.04 | 200 | 3.0150 | 1.0 |
| No log | 0.05 | 300 | 2.8622 | 1.0003 |
| No log | 0.07 | 400 | 0.9506 | 0.9771 |
| 5.1598 | 0.09 | 500 | 0.4883 | 1.0009 |
| 5.1598 | 0.11 | 600 | 0.3893 | 1.0203 |
| 5.1598 | 0.13 | 700 | 0.3417 | 1.0283 |
| 5.1598 | 0.14 | 800 | 0.3352 | 1.0335 |
| 5.1598 | 0.16 | 900 | 0.2987 | 1.0168 |
| 0.3671 | 0.18 | 1000 | 0.2921 | 1.0159 |
| 0.3671 | 0.2 | 1100 | 0.2770 | 1.0096 |
| 0.3671 | 0.22 | 1200 | 0.2790 | 1.0398 |
| 0.3671 | 0.24 | 1300 | 0.2659 | 1.0190 |
| 0.3671 | 0.25 | 1400 | 0.2657 | 1.0528 |
| 0.289 | 0.27 | 1500 | 0.2556 | 1.0301 |
| 0.289 | 0.29 | 1600 | 0.2514 | 1.0193 |
| 0.289 | 0.31 | 1700 | 0.2708 | 1.0699 |
| 0.289 | 0.33 | 1800 | 0.2455 | 1.0723 |
| 0.289 | 0.34 | 1900 | 0.2456 | 1.0100 |
| 0.271 | 0.36 | 2000 | 0.2338 | 1.0533 |
| 0.271 | 0.38 | 2100 | 0.2479 | 1.0128 |
| 0.271 | 0.4 | 2200 | 0.2483 | 1.0386 |
| 0.271 | 0.42 | 2300 | 0.2436 | 1.0528 |
| 0.271 | 0.43 | 2400 | 0.2382 | 1.0476 |
| 0.2634 | 0.45 | 2500 | 0.2329 | 1.0680 |
| 0.2634 | 0.47 | 2600 | 0.2433 | 1.0581 |
| 0.2634 | 0.49 | 2700 | 0.2354 | 1.0641 |
| 0.2634 | 0.51 | 2800 | 0.2318 | 1.0504 |
| 0.2634 | 0.52 | 2900 | 0.2325 | 1.0500 |
| 0.2522 | 0.54 | 3000 | 0.2344 | 1.0380 |
| 0.2522 | 0.56 | 3100 | 0.2244 | 1.0663 |
| 0.2522 | 0.58 | 3200 | 0.2340 | 1.0647 |
| 0.2522 | 0.6 | 3300 | 0.2288 | 1.0538 |
| 0.2522 | 0.61 | 3400 | 0.2212 | 1.0614 |
| 0.2468 | 0.63 | 3500 | 0.2487 | 1.0557 |
| 0.2468 | 0.65 | 3600 | 0.2330 | 1.0510 |
| 0.2468 | 0.67 | 3700 | 0.2308 | 1.0506 |
| 0.2468 | 0.69 | 3800 | 0.2320 | 1.0451 |
| 0.2468 | 0.71 | 3900 | 0.2261 | 1.0701 |
| 0.2505 | 0.72 | 4000 | 0.2281 | 1.0713 |
| 0.2505 | 0.74 | 4100 | 0.2277 | 1.0741 |
| 0.2505 | 0.76 | 4200 | 0.2253 | 1.0814 |
| 0.2505 | 0.78 | 4300 | 0.2215 | 1.0437 |
| 0.2505 | 0.8 | 4400 | 0.2220 | 1.0557 |
| 0.2434 | 0.81 | 4500 | 0.2184 | 1.0533 |
| 0.2434 | 0.83 | 4600 | 0.2222 | 1.0819 |
| 0.2434 | 0.85 | 4700 | 0.2162 | 1.0238 |
| 0.2434 | 0.87 | 4800 | 0.2132 | 1.0457 |
| 0.2434 | 0.89 | 4900 | 0.2068 | 1.0611 |
| 0.2347 | 0.9 | 5000 | 0.2166 | 1.0332 |
| 0.2347 | 0.92 | 5100 | 0.2087 | 1.0433 |
| 0.2347 | 0.94 | 5200 | 0.2100 | 1.0292 |
| 0.2347 | 0.96 | 5300 | 0.2067 | 1.0734 |
| 0.2347 | 0.98 | 5400 | 0.2148 | 1.0279 |
| 0.2333 | 0.99 | 5500 | 0.2125 | 1.0277 |
| 0.2333 | 1.01 | 5600 | 0.2054 | 1.0453 |
| 0.2333 | 1.03 | 5700 | 0.2091 | 1.0557 |
| 0.2333 | 1.05 | 5800 | 0.2086 | 1.0239 |
| 0.2333 | 1.07 | 5900 | 0.2051 | 1.0645 |
| 0.2087 | 1.09 | 6000 | 0.2103 | 1.0240 |
| 0.2087 | 1.1 | 6100 | 0.2145 | 1.0197 |
| 0.2087 | 1.12 | 6200 | 0.2136 | 1.0248 |
| 0.2087 | 1.14 | 6300 | 0.2045 | 1.0443 |
| 0.2087 | 1.16 | 6400 | 0.2089 | 1.0397 |
| 0.2013 | 1.18 | 6500 | 0.2012 | 1.0654 |
| 0.2013 | 1.19 | 6600 | 0.2054 | 1.0414 |
| 0.2013 | 1.21 | 6700 | 0.2081 | 1.0632 |
| 0.2013 | 1.23 | 6800 | 0.2104 | 1.0190 |
| 0.2013 | 1.25 | 6900 | 0.2045 | 1.0813 |
| 0.2092 | 1.27 | 7000 | 0.2096 | 1.0751 |
| 0.2092 | 1.28 | 7100 | 0.2103 | 1.0328 |
| 0.2092 | 1.3 | 7200 | 0.2044 | 1.0011 |
| 0.2092 | 1.32 | 7300 | 0.2089 | 1.0260 |
| 0.2092 | 1.34 | 7400 | 0.2063 | 1.0551 |
| 0.2076 | 1.36 | 7500 | 0.2029 | 1.0075 |
| 0.2076 | 1.37 | 7600 | 0.2040 | 1.0528 |
| 0.2076 | 1.39 | 7700 | 0.2075 | 1.0398 |
| 0.2076 | 1.41 | 7800 | 0.2023 | 1.0231 |
| 0.2076 | 1.43 | 7900 | 0.2049 | 1.0318 |
| 0.2028 | 1.45 | 8000 | 0.2072 | 1.0763 |
| 0.2028 | 1.47 | 8100 | 0.2075 | 1.0762 |
| 0.2028 | 1.48 | 8200 | 0.2052 | 1.0838 |
| 0.2028 | 1.5 | 8300 | 0.2053 | 1.0407 |
| 0.2028 | 1.52 | 8400 | 0.2066 | 1.0266 |
| 0.2025 | 1.54 | 8500 | 0.2037 | 1.0628 |
| 0.2025 | 1.56 | 8600 | 0.2010 | 1.0351 |
| 0.2025 | 1.57 | 8700 | 0.1961 | 1.0812 |
| 0.2025 | 1.59 | 8800 | 0.1963 | 1.0868 |
| 0.2025 | 1.61 | 8900 | 0.2022 | 1.0710 |
| 0.1997 | 1.63 | 9000 | 0.2051 | 1.0764 |
| 0.1997 | 1.65 | 9100 | 0.1987 | 1.0581 |
| 0.1997 | 1.66 | 9200 | 0.2051 | 1.0611 |
| 0.1997 | 1.68 | 9300 | 0.1999 | 1.0808 |
| 0.1997 | 1.7 | 9400 | 0.1972 | 1.0703 |
| 0.1983 | 1.72 | 9500 | 0.1961 | 1.0584 |
| 0.1983 | 1.74 | 9600 | 0.2031 | 1.0938 |
| 0.1983 | 1.75 | 9700 | 0.2019 | 1.0891 |
| 0.1983 | 1.77 | 9800 | 0.2006 | 1.0542 |
| 0.1983 | 1.79 | 9900 | 0.1925 | 1.0627 |
| 0.1961 | 1.81 | 10000 | 0.1976 | 1.0751 |
| 0.1961 | 1.83 | 10100 | 0.2051 | 1.0611 |
| 0.1961 | 1.85 | 10200 | 0.2037 | 1.0656 |
| 0.1961 | 1.86 | 10300 | 0.2025 | 1.0291 |
| 0.1961 | 1.88 | 10400 | 0.1977 | 1.0525 |
| 0.2025 | 1.9 | 10500 | 0.2030 | 1.0670 |
| 0.2025 | 1.92 | 10600 | 0.1980 | 1.0765 |
| 0.2025 | 1.94 | 10700 | 0.1975 | 1.0254 |
| 0.2025 | 1.95 | 10800 | 0.1986 | 1.0636 |
| 0.2025 | 1.97 | 10900 | 0.1956 | 1.0352 |
| 0.2025 | 1.99 | 11000 | 0.1954 | 1.0265 |
| 0.2025 | 2.01 | 11100 | 0.1957 | 1.0752 |
| 0.2025 | 2.03 | 11200 | 0.1943 | 1.0784 |
| 0.2025 | 2.04 | 11300 | 0.1898 | 1.0341 |
| 0.2025 | 2.06 | 11400 | 0.1921 | 1.0301 |
| 0.1805 | 2.08 | 11500 | 0.1910 | 1.0230 |
| 0.1805 | 2.1 | 11600 | 0.1961 | 1.0203 |
| 0.1805 | 2.12 | 11700 | 0.1973 | 1.0776 |
| 0.1805 | 2.13 | 11800 | 0.1876 | 1.0788 |
| 0.1805 | 2.15 | 11900 | 0.1934 | 1.0251 |
| 0.177 | 2.17 | 12000 | 0.1967 | 1.0340 |
| 0.177 | 2.19 | 12100 | 0.1932 | 1.0131 |
| 0.177 | 2.21 | 12200 | 0.1926 | 1.0078 |
| 0.177 | 2.23 | 12300 | 0.1947 | 0.9991 |
| 0.177 | 2.24 | 12400 | 0.1914 | 1.0213 |
| 0.1782 | 2.26 | 12500 | 0.1962 | 0.9882 |
| 0.1782 | 2.28 | 12600 | 0.1960 | 1.0562 |
| 0.1782 | 2.3 | 12700 | 0.2006 | 1.0401 |
| 0.1782 | 2.32 | 12800 | 0.1950 | 1.0688 |
| 0.1782 | 2.33 | 12900 | 0.1920 | 1.0435 |
| 0.1796 | 2.35 | 13000 | 0.1926 | 1.0667 |
| 0.1796 | 2.37 | 13100 | 0.1949 | 1.0859 |
| 0.1796 | 2.39 | 13200 | 0.1932 | 1.0670 |
| 0.1796 | 2.41 | 13300 | 0.1882 | 1.0663 |
| 0.1796 | 2.42 | 13400 | 0.1877 | 1.0760 |
| 0.1775 | 2.44 | 13500 | 0.1893 | 1.0859 |
| 0.1775 | 2.46 | 13600 | 0.1936 | 1.0702 |
| 0.1775 | 2.48 | 13700 | 0.1871 | 1.0414 |
| 0.1775 | 2.5 | 13800 | 0.1917 | 1.0430 |
| 0.1775 | 2.51 | 13900 | 0.1922 | 1.0422 |
| 0.1778 | 2.53 | 14000 | 0.1875 | 1.0585 |
| 0.1778 | 2.55 | 14100 | 0.1876 | 1.0603 |
| 0.1778 | 2.57 | 14200 | 0.1888 | 1.0628 |
| 0.1778 | 2.59 | 14300 | 0.1948 | 1.0782 |
| 0.1778 | 2.6 | 14400 | 0.1942 | 1.0695 |
| 0.1784 | 2.62 | 14500 | 0.1842 | 1.0863 |
| 0.1784 | 2.64 | 14600 | 0.1850 | 1.0543 |
| 0.1784 | 2.66 | 14700 | 0.1824 | 1.0683 |
| 0.1784 | 2.68 | 14800 | 0.1888 | 1.0693 |
| 0.1784 | 2.7 | 14900 | 0.1871 | 1.0175 |
| 0.1753 | 2.71 | 15000 | 0.1889 | 1.0549 |
| 0.1753 | 2.73 | 15100 | 0.1865 | 1.0544 |
| 0.1753 | 2.75 | 15200 | 0.1918 | 1.0726 |
| 0.1753 | 2.77 | 15300 | 0.1964 | 1.0915 |
| 0.1753 | 2.79 | 15400 | 0.1900 | 1.0610 |
| 0.1768 | 2.8 | 15500 | 0.1894 | 1.0763 |
| 0.1768 | 2.82 | 15600 | 0.1882 | 1.0548 |
| 0.1768 | 2.84 | 15700 | 0.1861 | 1.0902 |
| 0.1768 | 2.86 | 15800 | 0.1860 | 1.0551 |
| 0.1768 | 2.88 | 15900 | 0.1879 | 1.0581 |
| 0.1761 | 2.89 | 16000 | 0.1899 | 1.0544 |
| 0.1761 | 2.91 | 16100 | 0.1860 | 1.0530 |
| 0.1761 | 2.93 | 16200 | 0.1894 | 1.0596 |
| 0.1761 | 2.95 | 16300 | 0.1835 | 1.0394 |
| 0.1761 | 2.97 | 16400 | 0.1852 | 1.0445 |
| 0.1754 | 2.98 | 16500 | 0.1847 | 1.0390 |
| 0.1754 | 3.0 | 16600 | 0.1828 | 1.0440 |
| 0.1754 | 3.02 | 16700 | 0.1869 | 1.0560 |
| 0.1754 | 3.04 | 16800 | 0.1882 | 1.0573 |
| 0.1754 | 3.06 | 16900 | 0.1912 | 1.0600 |
| 0.1592 | 3.08 | 17000 | 0.1921 | 1.0529 |
| 0.1592 | 3.09 | 17100 | 0.1881 | 1.0175 |
| 0.1592 | 3.11 | 17200 | 0.1891 | 1.0654 |
| 0.1592 | 3.13 | 17300 | 0.1889 | 1.0687 |
| 0.1592 | 3.15 | 17400 | 0.1916 | 1.0642 |
| 0.1556 | 3.17 | 17500 | 0.1850 | 1.0295 |
| 0.1556 | 3.18 | 17600 | 0.1875 | 1.0273 |
| 0.1556 | 3.2 | 17700 | 0.1894 | 1.0051 |
| 0.1556 | 3.22 | 17800 | 0.1870 | 1.0462 |
| 0.1556 | 3.24 | 17900 | 0.1831 | 1.0308 |
| 0.1557 | 3.26 | 18000 | 0.1878 | 1.0603 |
| 0.1557 | 3.27 | 18100 | 0.1850 | 1.0566 |
| 0.1557 | 3.29 | 18200 | 0.1843 | 1.0629 |
| 0.1557 | 3.31 | 18300 | 0.1886 | 1.0378 |
| 0.1557 | 3.33 | 18400 | 0.1892 | 1.0381 |
| 0.159 | 3.35 | 18500 | 0.1942 | 1.0519 |
| 0.159 | 3.36 | 18600 | 0.1829 | 1.0622 |
| 0.159 | 3.38 | 18700 | 0.1894 | 1.0557 |
| 0.159 | 3.4 | 18800 | 0.1895 | 1.0627 |
| 0.159 | 3.42 | 18900 | 0.1863 | 1.0362 |
| 0.1582 | 3.44 | 19000 | 0.1888 | 1.0491 |
| 0.1582 | 3.46 | 19100 | 0.1854 | 1.0483 |
| 0.1582 | 3.47 | 19200 | 0.1797 | 0.9787 |
| 0.1582 | 3.49 | 19300 | 0.1785 | 1.0086 |
| 0.1582 | 3.51 | 19400 | 0.1797 | 0.9915 |
| 0.1507 | 3.53 | 19500 | 0.1873 | 1.0266 |
| 0.1507 | 3.55 | 19600 | 0.1838 | 1.0299 |
| 0.1507 | 3.56 | 19700 | 0.1817 | 1.0355 |
| 0.1507 | 3.58 | 19800 | 0.1819 | 1.0271 |
| 0.1507 | 3.6 | 19900 | 0.1883 | 1.0248 |
| 0.1601 | 3.62 | 20000 | 0.1823 | 1.0406 |
| 0.1601 | 3.64 | 20100 | 0.1801 | 1.0261 |
| 0.1601 | 3.65 | 20200 | 0.1783 | 1.0329 |
| 0.1601 | 3.67 | 20300 | 0.1857 | 1.0162 |
| 0.1601 | 3.69 | 20400 | 0.1814 | 1.0212 |
| 0.1552 | 3.71 | 20500 | 0.1837 | 1.0232 |
| 0.1552 | 3.73 | 20600 | 0.1843 | 1.0314 |
| 0.1552 | 3.74 | 20700 | 0.1842 | 1.0258 |
| 0.1552 | 3.76 | 20800 | 0.1821 | 1.0479 |
| 0.1552 | 3.78 | 20900 | 0.1864 | 1.0459 |
| 0.1576 | 3.8 | 21000 | 0.1831 | 1.0364 |
| 0.1576 | 3.82 | 21100 | 0.1852 | 1.0271 |
| 0.1576 | 3.83 | 21200 | 0.1865 | 1.0204 |
| 0.1576 | 3.85 | 21300 | 0.1794 | 1.0324 |
| 0.1576 | 3.87 | 21400 | 0.1826 | 1.0315 |
| 0.1585 | 3.89 | 21500 | 0.1824 | 1.0327 |
| 0.1585 | 3.91 | 21600 | 0.1838 | 1.0208 |
| 0.1585 | 3.93 | 21700 | 0.1850 | 1.0199 |
| 0.1585 | 3.94 | 21800 | 0.1841 | 1.0050 |
| 0.1585 | 3.96 | 21900 | 0.1783 | 1.0003 |
| 0.1572 | 3.98 | 22000 | 0.1787 | 1.0115 |
| 0.1572 | 4.0 | 22100 | 0.1810 | 1.0235 |
| 0.1572 | 4.02 | 22200 | 0.1763 | 1.0191 |
| 0.1572 | 4.03 | 22300 | 0.1764 | 1.0332 |
| 0.1572 | 4.05 | 22400 | 0.1794 | 1.0429 |
| 0.1406 | 4.07 | 22500 | 0.1905 | 1.0288 |
| 0.1406 | 4.09 | 22600 | 0.1776 | 1.0244 |
| 0.1406 | 4.11 | 22700 | 0.1782 | 1.0451 |
| 0.1406 | 4.12 | 22800 | 0.1771 | 1.0387 |
| 0.1406 | 4.14 | 22900 | 0.1788 | 1.0435 |
| 0.14 | 4.16 | 23000 | 0.1792 | 1.0421 |
| 0.14 | 4.18 | 23100 | 0.1841 | 1.0241 |
| 0.14 | 4.2 | 23200 | 0.1769 | 1.0546 |
| 0.14 | 4.21 | 23300 | 0.1815 | 1.0602 |
| 0.14 | 4.23 | 23400 | 0.1784 | 1.0369 |
| 0.1394 | 4.25 | 23500 | 0.1809 | 1.0406 |
| 0.1394 | 4.27 | 23600 | 0.1744 | 1.0133 |
| 0.1394 | 4.29 | 23700 | 0.1771 | 1.0214 |
| 0.1394 | 4.31 | 23800 | 0.1765 | 1.0064 |
| 0.1394 | 4.32 | 23900 | 0.1793 | 1.0200 |
| 0.14 | 4.34 | 24000 | 0.1776 | 1.0352 |
| 0.14 | 4.36 | 24100 | 0.1775 | 1.0294 |
| 0.14 | 4.38 | 24200 | 0.1763 | 1.0213 |
| 0.14 | 4.4 | 24300 | 0.1697 | 1.0302 |
| 0.14 | 4.41 | 24400 | 0.1771 | 1.0259 |
| 0.1408 | 4.43 | 24500 | 0.1747 | 1.0409 |
| 0.1408 | 4.45 | 24600 | 0.1769 | 1.0278 |
| 0.1408 | 4.47 | 24700 | 0.1767 | 1.0190 |
| 0.1408 | 4.49 | 24800 | 0.1745 | 1.0281 |
| 0.1408 | 4.5 | 24900 | 0.1738 | 1.0356 |
| 0.1391 | 4.52 | 25000 | 0.1781 | 1.0429 |
| 0.1391 | 4.54 | 25100 | 0.1784 | 1.0076 |
| 0.1391 | 4.56 | 25200 | 0.1771 | 1.0157 |
| 0.1391 | 4.58 | 25300 | 0.1758 | 1.0337 |
| 0.1391 | 4.59 | 25400 | 0.1758 | 1.0466 |
| 0.1398 | 4.61 | 25500 | 0.1724 | 1.0403 |
| 0.1398 | 4.63 | 25600 | 0.1765 | 1.0481 |
| 0.1398 | 4.65 | 25700 | 0.1757 | 1.0320 |
| 0.1398 | 4.67 | 25800 | 0.1814 | 1.0479 |
| 0.1398 | 4.69 | 25900 | 0.1713 | 1.0251 |
| 0.1427 | 4.7 | 26000 | 0.1735 | 1.0340 |
| 0.1427 | 4.72 | 26100 | 0.1765 | 1.0358 |
| 0.1427 | 4.74 | 26200 | 0.1731 | 1.0220 |
| 0.1427 | 4.76 | 26300 | 0.1769 | 1.0261 |
| 0.1427 | 4.78 | 26400 | 0.1747 | 1.0139 |
| 0.1424 | 4.79 | 26500 | 0.1791 | 1.0406 |
| 0.1424 | 4.81 | 26600 | 0.1735 | 1.0497 |
| 0.1424 | 4.83 | 26700 | 0.1710 | 1.0433 |
| 0.1424 | 4.85 | 26800 | 0.1771 | 1.0002 |
| 0.1424 | 4.87 | 26900 | 0.1748 | 1.0046 |
| 0.1419 | 4.88 | 27000 | 0.1794 | 1.0332 |
| 0.1419 | 4.9 | 27100 | 0.1772 | 1.0558 |
| 0.1419 | 4.92 | 27200 | 0.1757 | 1.0477 |
| 0.1419 | 4.94 | 27300 | 0.1735 | 1.0324 |
| 0.1419 | 4.96 | 27400 | 0.1758 | 1.0260 |
| 0.1433 | 4.97 | 27500 | 0.1767 | 1.0422 |
| 0.1433 | 4.99 | 27600 | 0.1695 | 1.0386 |
| 0.1433 | 5.01 | 27700 | 0.1763 | 1.0571 |
| 0.1433 | 5.03 | 27800 | 0.1743 | 1.0367 |
| 0.1433 | 5.05 | 27900 | 0.1804 | 1.0255 |
| 0.1306 | 5.07 | 28000 | 0.1803 | 1.0377 |
| 0.1306 | 5.08 | 28100 | 0.1750 | 1.0552 |
| 0.1306 | 5.1 | 28200 | 0.1743 | 1.0512 |
| 0.1306 | 5.12 | 28300 | 0.1777 | 1.0584 |
| 0.1306 | 5.14 | 28400 | 0.1726 | 1.0374 |
| 0.123 | 5.16 | 28500 | 0.1776 | 1.0439 |
| 0.123 | 5.17 | 28600 | 0.1759 | 1.0682 |
| 0.123 | 5.19 | 28700 | 0.1724 | 1.0511 |
| 0.123 | 5.21 | 28800 | 0.1677 | 1.0560 |
| 0.123 | 5.23 | 28900 | 0.1699 | 1.0421 |
| 0.1217 | 5.25 | 29000 | 0.1803 | 1.0370 |
| 0.1217 | 5.26 | 29100 | 0.1770 | 1.0474 |
| 0.1217 | 5.28 | 29200 | 0.1733 | 1.0332 |
| 0.1217 | 5.3 | 29300 | 0.1746 | 1.0158 |
| 0.1217 | 5.32 | 29400 | 0.1763 | 1.0341 |
| 0.1246 | 5.34 | 29500 | 0.1775 | 1.0348 |
| 0.1246 | 5.35 | 29600 | 0.1730 | 1.0492 |
| 0.1246 | 5.37 | 29700 | 0.1730 | 1.0503 |
| 0.1246 | 5.39 | 29800 | 0.1727 | 1.0437 |
| 0.1246 | 5.41 | 29900 | 0.1744 | 1.0539 |
| 0.127 | 5.43 | 30000 | 0.1748 | 1.0463 |
| 0.127 | 5.44 | 30100 | 0.1746 | 1.0555 |
| 0.127 | 5.46 | 30200 | 0.1810 | 1.0558 |
| 0.127 | 5.48 | 30300 | 0.1773 | 1.0407 |
| 0.127 | 5.5 | 30400 | 0.1722 | 1.0489 |
| 0.1276 | 5.52 | 30500 | 0.1720 | 1.0520 |
| 0.1276 | 5.54 | 30600 | 0.1777 | 1.0347 |
| 0.1276 | 5.55 | 30700 | 0.1685 | 1.0347 |
| 0.1276 | 5.57 | 30800 | 0.1659 | 1.0338 |
| 0.1276 | 5.59 | 30900 | 0.1756 | 1.0228 |
| 0.1246 | 5.61 | 31000 | 0.1717 | 1.0409 |
| 0.1246 | 5.63 | 31100 | 0.1764 | 1.0202 |
| 0.1246 | 5.64 | 31200 | 0.1693 | 1.0314 |
| 0.1246 | 5.66 | 31300 | 0.1731 | 1.0319 |
| 0.1246 | 5.68 | 31400 | 0.1688 | 1.0380 |
| 0.1271 | 5.7 | 31500 | 0.1671 | 1.0350 |
| 0.1271 | 5.72 | 31600 | 0.1676 | 1.0430 |
| 0.1271 | 5.73 | 31700 | 0.1656 | 1.0441 |
| 0.1271 | 5.75 | 31800 | 0.1664 | 1.0403 |
| 0.1271 | 5.77 | 31900 | 0.1691 | 1.0152 |
| 0.1259 | 5.79 | 32000 | 0.1702 | 1.0018 |
| 0.1259 | 5.81 | 32100 | 0.1664 | 1.0246 |
| 0.1259 | 5.82 | 32200 | 0.1737 | 1.0340 |
| 0.1259 | 5.84 | 32300 | 0.1742 | 1.0449 |
| 0.1259 | 5.86 | 32400 | 0.1707 | 1.0279 |
| 0.1273 | 5.88 | 32500 | 0.1697 | 1.0471 |
| 0.1273 | 5.9 | 32600 | 0.1668 | 1.0322 |
| 0.1273 | 5.92 | 32700 | 0.1706 | 1.0378 |
| 0.1273 | 5.93 | 32800 | 0.1704 | 1.0350 |
| 0.1273 | 5.95 | 32900 | 0.1725 | 1.0244 |
| 0.123 | 5.97 | 33000 | 0.1678 | 1.0447 |
| 0.123 | 5.99 | 33100 | 0.1681 | 1.0438 |
| 0.123 | 6.01 | 33200 | 0.1689 | 1.0297 |
| 0.123 | 6.02 | 33300 | 0.1690 | 1.0333 |
| 0.123 | 6.04 | 33400 | 0.1734 | 1.0296 |
| 0.1163 | 6.06 | 33500 | 0.1748 | 1.0307 |
| 0.1163 | 6.08 | 33600 | 0.1715 | 1.0123 |
| 0.1163 | 6.1 | 33700 | 0.1668 | 1.0117 |
| 0.1163 | 6.11 | 33800 | 0.1690 | 1.0230 |
| 0.1163 | 6.13 | 33900 | 0.1693 | 1.0166 |
| 0.1101 | 6.15 | 34000 | 0.1728 | 1.0162 |
| 0.1101 | 6.17 | 34100 | 0.1683 | 1.0107 |
| 0.1101 | 6.19 | 34200 | 0.1703 | 0.9814 |
| 0.1101 | 6.2 | 34300 | 0.1692 | 1.0007 |
| 0.1101 | 6.22 | 34400 | 0.1690 | 1.0000 |
| 0.1118 | 6.24 | 34500 | 0.1734 | 0.9972 |
| 0.1118 | 6.26 | 34600 | 0.1739 | 1.0096 |
| 0.1118 | 6.28 | 34700 | 0.1749 | 1.0047 |
| 0.1118 | 6.3 | 34800 | 0.1709 | 1.0111 |
| 0.1118 | 6.31 | 34900 | 0.1717 | 1.0179 |
| 0.1153 | 6.33 | 35000 | 0.1690 | 1.0155 |
| 0.1153 | 6.35 | 35100 | 0.1710 | 1.0144 |
| 0.1153 | 6.37 | 35200 | 0.1719 | 1.0030 |
| 0.1153 | 6.39 | 35300 | 0.1690 | 1.0272 |
| 0.1153 | 6.4 | 35400 | 0.1673 | 1.0103 |
| 0.1106 | 6.42 | 35500 | 0.1710 | 1.0222 |
| 0.1106 | 6.44 | 35600 | 0.1747 | 1.0173 |
| 0.1106 | 6.46 | 35700 | 0.1721 | 0.9933 |
| 0.1106 | 6.48 | 35800 | 0.1670 | 1.0184 |
| 0.1106 | 6.49 | 35900 | 0.1714 | 1.0122 |
| 0.1116 | 6.51 | 36000 | 0.1717 | 1.0035 |
| 0.1116 | 6.53 | 36100 | 0.1685 | 1.0099 |
| 0.1116 | 6.55 | 36200 | 0.1687 | 1.0288 |
| 0.1116 | 6.57 | 36300 | 0.1664 | 1.0314 |
| 0.1116 | 6.58 | 36400 | 0.1665 | 1.0264 |
| 0.1128 | 6.6 | 36500 | 0.1681 | 1.0420 |
| 0.1128 | 6.62 | 36600 | 0.1682 | 1.0409 |
| 0.1128 | 6.64 | 36700 | 0.1717 | 1.0271 |
| 0.1128 | 6.66 | 36800 | 0.1717 | 1.0166 |
| 0.1128 | 6.68 | 36900 | 0.1755 | 1.0175 |
| 0.1134 | 6.69 | 37000 | 0.1623 | 1.0185 |
| 0.1134 | 6.71 | 37100 | 0.1674 | 1.0302 |
| 0.1134 | 6.73 | 37200 | 0.1633 | 1.0325 |
| 0.1134 | 6.75 | 37300 | 0.1628 | 1.0228 |
| 0.1134 | 6.77 | 37400 | 0.1636 | 1.0243 |
| 0.1102 | 6.78 | 37500 | 0.1667 | 1.0282 |
| 0.1102 | 6.8 | 37600 | 0.1623 | 1.0212 |
| 0.1102 | 6.82 | 37700 | 0.1639 | 1.0140 |
| 0.1102 | 6.84 | 37800 | 0.1587 | 1.0258 |
| 0.1102 | 6.86 | 37900 | 0.1610 | 1.0087 |
| 0.1113 | 6.87 | 38000 | 0.1647 | 1.0199 |
| 0.1113 | 6.89 | 38100 | 0.1609 | 1.0054 |
| 0.1113 | 6.91 | 38200 | 0.1602 | 1.0145 |
| 0.1113 | 6.93 | 38300 | 0.1602 | 1.0144 |
| 0.1113 | 6.95 | 38400 | 0.1602 | 1.0375 |
| 0.1071 | 6.96 | 38500 | 0.1592 | 1.0259 |
| 0.1071 | 6.98 | 38600 | 0.1612 | 1.0236 |
| 0.1071 | 7.0 | 38700 | 0.1621 | 1.0277 |
| 0.1071 | 7.02 | 38800 | 0.1669 | 1.0367 |
| 0.1071 | 7.04 | 38900 | 0.1742 | 1.0484 |
| 0.1062 | 7.05 | 39000 | 0.1752 | 1.0302 |
| 0.1062 | 7.07 | 39100 | 0.1676 | 1.0244 |
| 0.1062 | 7.09 | 39200 | 0.1723 | 1.0300 |
| 0.1062 | 7.11 | 39300 | 0.1727 | 1.0294 |
| 0.1062 | 7.13 | 39400 | 0.1711 | 1.0255 |
| 0.1021 | 7.15 | 39500 | 0.1699 | 1.0471 |
| 0.1021 | 7.16 | 39600 | 0.1682 | 1.0426 |
| 0.1021 | 7.18 | 39700 | 0.1713 | 1.0233 |
| 0.1021 | 7.2 | 39800 | 0.1682 | 1.0259 |
| 0.1021 | 7.22 | 39900 | 0.1710 | 1.0162 |
| 0.103 | 7.24 | 40000 | 0.1725 | 1.0283 |
| 0.103 | 7.25 | 40100 | 0.1729 | 1.0264 |
| 0.103 | 7.27 | 40200 | 0.1665 | 1.0451 |
| 0.103 | 7.29 | 40300 | 0.1671 | 1.0386 |
| 0.103 | 7.31 | 40400 | 0.1671 | 1.0316 |
| 0.0981 | 7.33 | 40500 | 0.1708 | 1.0257 |
| 0.0981 | 7.34 | 40600 | 0.1642 | 1.0152 |
| 0.0981 | 7.36 | 40700 | 0.1707 | 1.0110 |
| 0.0981 | 7.38 | 40800 | 0.1675 | 1.0186 |
| 0.0981 | 7.4 | 40900 | 0.1702 | 1.0123 |
| 0.1005 | 7.42 | 41000 | 0.1699 | 1.0159 |
| 0.1005 | 7.43 | 41100 | 0.1703 | 1.0219 |
| 0.1005 | 7.45 | 41200 | 0.1707 | 1.0194 |
| 0.1005 | 7.47 | 41300 | 0.1644 | 1.0016 |
| 0.1005 | 7.49 | 41400 | 0.1716 | 0.9941 |
| 0.1021 | 7.51 | 41500 | 0.1670 | 1.0159 |
| 0.1021 | 7.53 | 41600 | 0.1667 | 1.0033 |
| 0.1021 | 7.54 | 41700 | 0.1667 | 1.0176 |
| 0.1021 | 7.56 | 41800 | 0.1679 | 1.0194 |
| 0.1021 | 7.58 | 41900 | 0.1632 | 1.0418 |
| 0.0963 | 7.6 | 42000 | 0.1712 | 1.0152 |
| 0.0963 | 7.62 | 42100 | 0.1632 | 1.0364 |
| 0.0963 | 7.63 | 42200 | 0.1702 | 1.0229 |
| 0.0963 | 7.65 | 42300 | 0.1655 | 1.0179 |
| 0.0963 | 7.67 | 42400 | 0.1698 | 1.0329 |
| 0.1014 | 7.69 | 42500 | 0.1691 | 1.0398 |
| 0.1014 | 7.71 | 42600 | 0.1638 | 1.0487 |
| 0.1014 | 7.72 | 42700 | 0.1617 | 1.0210 |
| 0.1014 | 7.74 | 42800 | 0.1648 | 1.0124 |
| 0.1014 | 7.76 | 42900 | 0.1608 | 1.0202 |
| 0.1008 | 7.78 | 43000 | 0.1611 | 1.0353 |
| 0.1008 | 7.8 | 43100 | 0.1633 | 1.0319 |
| 0.1008 | 7.81 | 43200 | 0.1640 | 1.0032 |
| 0.1008 | 7.83 | 43300 | 0.1589 | 0.9985 |
| 0.1008 | 7.85 | 43400 | 0.1630 | 0.9975 |
| 0.0988 | 7.87 | 43500 | 0.1604 | 1.0053 |
| 0.0988 | 7.89 | 43600 | 0.1687 | 1.0063 |
| 0.0988 | 7.91 | 43700 | 0.1619 | 1.0096 |
| 0.0988 | 7.92 | 43800 | 0.1565 | 0.9901 |
| 0.0988 | 7.94 | 43900 | 0.1619 | 0.9742 |
| 0.102 | 7.96 | 44000 | 0.1598 | 0.9593 |
| 0.102 | 7.98 | 44100 | 0.1635 | 0.9718 |
| 0.102 | 8.0 | 44200 | 0.1624 | 0.9903 |
| 0.102 | 8.01 | 44300 | 0.1605 | 0.9882 |
| 0.102 | 8.03 | 44400 | 0.1657 | 1.0128 |
| 0.0961 | 8.05 | 44500 | 0.1651 | 1.0155 |
| 0.0961 | 8.07 | 44600 | 0.1680 | 1.0194 |
| 0.0961 | 8.09 | 44700 | 0.1694 | 1.0112 |
| 0.0961 | 8.1 | 44800 | 0.1665 | 1.0073 |
| 0.0961 | 8.12 | 44900 | 0.1612 | 1.0200 |
| 0.0894 | 8.14 | 45000 | 0.1652 | 1.0337 |
| 0.0894 | 8.16 | 45100 | 0.1626 | 1.0086 |
| 0.0894 | 8.18 | 45200 | 0.1639 | 1.0083 |
| 0.0894 | 8.19 | 45300 | 0.1634 | 1.0223 |
| 0.0894 | 8.21 | 45400 | 0.1631 | 1.0339 |
| 0.0887 | 8.23 | 45500 | 0.1640 | 1.0311 |
| 0.0887 | 8.25 | 45600 | 0.1661 | 1.0264 |
| 0.0887 | 8.27 | 45700 | 0.1650 | 1.0315 |
| 0.0887 | 8.29 | 45800 | 0.1624 | 1.0390 |
| 0.0887 | 8.3 | 45900 | 0.1624 | 1.0350 |
| 0.0884 | 8.32 | 46000 | 0.1615 | 1.0318 |
| 0.0884 | 8.34 | 46100 | 0.1628 | 1.0410 |
| 0.0884 | 8.36 | 46200 | 0.1627 | 1.0429 |
| 0.0884 | 8.38 | 46300 | 0.1644 | 1.0320 |
| 0.0884 | 8.39 | 46400 | 0.1633 | 1.0177 |
| 0.0893 | 8.41 | 46500 | 0.1654 | 1.0189 |
| 0.0893 | 8.43 | 46600 | 0.1598 | 1.0154 |
| 0.0893 | 8.45 | 46700 | 0.1618 | 1.0250 |
| 0.0893 | 8.47 | 46800 | 0.1639 | 1.0402 |
| 0.0893 | 8.48 | 46900 | 0.1616 | 1.0336 |
| 0.0869 | 8.5 | 47000 | 0.1613 | 1.0296 |
| 0.0869 | 8.52 | 47100 | 0.1648 | 1.0568 |
| 0.0869 | 8.54 | 47200 | 0.1625 | 1.0256 |
| 0.0869 | 8.56 | 47300 | 0.1609 | 1.0390 |
| 0.0869 | 8.57 | 47400 | 0.1606 | 1.0450 |
| 0.0894 | 8.59 | 47500 | 0.1605 | 1.0445 |
| 0.0894 | 8.61 | 47600 | 0.1660 | 1.0402 |
| 0.0894 | 8.63 | 47700 | 0.1618 | 1.0444 |
| 0.0894 | 8.65 | 47800 | 0.1669 | 1.0333 |
| 0.0894 | 8.66 | 47900 | 0.1627 | 1.0364 |
| 0.0885 | 8.68 | 48000 | 0.1616 | 1.0334 |
| 0.0885 | 8.7 | 48100 | 0.1626 | 1.0564 |
| 0.0885 | 8.72 | 48200 | 0.1624 | 1.0396 |
| 0.0885 | 8.74 | 48300 | 0.1623 | 1.0396 |
| 0.0885 | 8.76 | 48400 | 0.1612 | 1.0112 |
| 0.0888 | 8.77 | 48500 | 0.1638 | 1.0292 |
| 0.0888 | 8.79 | 48600 | 0.1639 | 0.9988 |
| 0.0888 | 8.81 | 48700 | 0.1618 | 1.0127 |
| 0.0888 | 8.83 | 48800 | 0.1584 | 1.0042 |
| 0.0888 | 8.85 | 48900 | 0.1615 | 1.0041 |
| 0.0887 | 8.86 | 49000 | 0.1637 | 1.0269 |
| 0.0887 | 8.88 | 49100 | 0.1627 | 0.9989 |
| 0.0887 | 8.9 | 49200 | 0.1583 | 1.0104 |
| 0.0887 | 8.92 | 49300 | 0.1600 | 1.0214 |
| 0.0887 | 8.94 | 49400 | 0.1599 | 1.0126 |
| 0.0893 | 8.95 | 49500 | 0.1595 | 1.0516 |
| 0.0893 | 8.97 | 49600 | 0.1625 | 1.0464 |
| 0.0893 | 8.99 | 49700 | 0.1595 | 1.0361 |
| 0.0893 | 9.01 | 49800 | 0.1614 | 1.0469 |
| 0.0893 | 9.03 | 49900 | 0.1612 | 1.0304 |
| 0.0834 | 9.04 | 50000 | 0.1643 | 1.0335 |
| 0.0834 | 9.06 | 50100 | 0.1640 | 1.0175 |
| 0.0834 | 9.08 | 50200 | 0.1655 | 1.0264 |
| 0.0834 | 9.1 | 50300 | 0.1678 | 1.0243 |
| 0.0834 | 9.12 | 50400 | 0.1659 | 1.0145 |
| 0.079 | 9.14 | 50500 | 0.1644 | 1.0316 |
| 0.079 | 9.15 | 50600 | 0.1630 | 1.0326 |
| 0.079 | 9.17 | 50700 | 0.1634 | 1.0154 |
| 0.079 | 9.19 | 50800 | 0.1697 | 1.0095 |
| 0.079 | 9.21 | 50900 | 0.1678 | 1.0050 |
| 0.078 | 9.23 | 51000 | 0.1626 | 1.0159 |
| 0.078 | 9.24 | 51100 | 0.1666 | 1.0238 |
| 0.078 | 9.26 | 51200 | 0.1644 | 1.0244 |
| 0.078 | 9.28 | 51300 | 0.1655 | 1.0345 |
| 0.078 | 9.3 | 51400 | 0.1615 | 1.0237 |
| 0.0776 | 9.32 | 51500 | 0.1664 | 1.0180 |
| 0.0776 | 9.33 | 51600 | 0.1603 | 1.0208 |
| 0.0776 | 9.35 | 51700 | 0.1594 | 1.0230 |
| 0.0776 | 9.37 | 51800 | 0.1622 | 1.0201 |
| 0.0776 | 9.39 | 51900 | 0.1596 | 1.0039 |
| 0.0782 | 9.41 | 52000 | 0.1645 | 1.0204 |
| 0.0782 | 9.42 | 52100 | 0.1640 | 1.0318 |
| 0.0782 | 9.44 | 52200 | 0.1621 | 1.0290 |
| 0.0782 | 9.46 | 52300 | 0.1638 | 1.0318 |
| 0.0782 | 9.48 | 52400 | 0.1613 | 1.0217 |
| 0.0782 | 9.5 | 52500 | 0.1609 | 1.0261 |
| 0.0782 | 9.52 | 52600 | 0.1625 | 1.0101 |
| 0.0782 | 9.53 | 52700 | 0.1613 | 1.0058 |
| 0.0782 | 9.55 | 52800 | 0.1599 | 1.0068 |
| 0.0782 | 9.57 | 52900 | 0.1600 | 1.0110 |
| 0.0797 | 9.59 | 53000 | 0.1594 | 1.0171 |
| 0.0797 | 9.61 | 53100 | 0.1583 | 1.0124 |
| 0.0797 | 9.62 | 53200 | 0.1646 | 1.0093 |
| 0.0797 | 9.64 | 53300 | 0.1580 | 1.0201 |
| 0.0797 | 9.66 | 53400 | 0.1599 | 1.0207 |
| 0.0783 | 9.68 | 53500 | 0.1577 | 1.0226 |
| 0.0783 | 9.7 | 53600 | 0.1593 | 1.0160 |
| 0.0783 | 9.71 | 53700 | 0.1570 | 1.0173 |
| 0.0783 | 9.73 | 53800 | 0.1614 | 1.0299 |
| 0.0783 | 9.75 | 53900 | 0.1610 | 1.0184 |
| 0.0779 | 9.77 | 54000 | 0.1606 | 1.0173 |
| 0.0779 | 9.79 | 54100 | 0.1577 | 1.0032 |
| 0.0779 | 9.8 | 54200 | 0.1590 | 1.0070 |
| 0.0779 | 9.82 | 54300 | 0.1580 | 1.0257 |
| 0.0779 | 9.84 | 54400 | 0.1592 | 1.0108 |
| 0.0778 | 9.86 | 54500 | 0.1617 | 0.9907 |
| 0.0778 | 9.88 | 54600 | 0.1605 | 1.0189 |
| 0.0778 | 9.89 | 54700 | 0.1605 | 1.0177 |
| 0.0778 | 9.91 | 54800 | 0.1536 | 1.0275 |
| 0.0778 | 9.93 | 54900 | 0.1658 | 1.0282 |
| 0.0777 | 9.95 | 55000 | 0.1543 | 1.0385 |
| 0.0777 | 9.97 | 55100 | 0.1559 | 1.0375 |
| 0.0777 | 9.99 | 55200 | 0.1590 | 1.0215 |
| 0.0777 | 10.0 | 55300 | 0.1624 | 1.0242 |
| 0.0777 | 10.02 | 55400 | 0.1635 | 1.0244 |
| 0.0712 | 10.04 | 55500 | 0.1629 | 1.0298 |
| 0.0712 | 10.06 | 55600 | 0.1601 | 1.0299 |
| 0.0712 | 10.08 | 55700 | 0.1625 | 1.0117 |
| 0.0712 | 10.09 | 55800 | 0.1650 | 1.0233 |
| 0.0712 | 10.11 | 55900 | 0.1631 | 1.0061 |
| 0.0667 | 10.13 | 56000 | 0.1637 | 1.0226 |
| 0.0667 | 10.15 | 56100 | 0.1607 | 1.0042 |
| 0.0667 | 10.17 | 56200 | 0.1599 | 1.0117 |
| 0.0667 | 10.18 | 56300 | 0.1623 | 1.0246 |
| 0.0667 | 10.2 | 56400 | 0.1639 | 1.0294 |
| 0.0695 | 10.22 | 56500 | 0.1650 | 1.0232 |
| 0.0695 | 10.24 | 56600 | 0.1620 | 1.0289 |
| 0.0695 | 10.26 | 56700 | 0.1667 | 1.0209 |
| 0.0695 | 10.27 | 56800 | 0.1580 | 1.0163 |
| 0.0695 | 10.29 | 56900 | 0.1646 | 1.0293 |
| 0.0686 | 10.31 | 57000 | 0.1636 | 1.0106 |
| 0.0686 | 10.33 | 57100 | 0.1586 | 1.0044 |
| 0.0686 | 10.35 | 57200 | 0.1582 | 1.0213 |
| 0.0686 | 10.37 | 57300 | 0.1627 | 1.0151 |
| 0.0686 | 10.38 | 57400 | 0.1619 | 1.0248 |
| 0.0686 | 10.4 | 57500 | 0.1596 | 1.0098 |
| 0.0686 | 10.42 | 57600 | 0.1606 | 1.0031 |
| 0.0686 | 10.44 | 57700 | 0.1620 | 1.0046 |
| 0.0686 | 10.46 | 57800 | 0.1592 | 1.0018 |
| 0.0686 | 10.47 | 57900 | 0.1592 | 1.0058 |
| 0.0669 | 10.49 | 58000 | 0.1605 | 0.9961 |
| 0.0669 | 10.51 | 58100 | 0.1632 | 1.0102 |
| 0.0669 | 10.53 | 58200 | 0.1593 | 1.0061 |
| 0.0669 | 10.55 | 58300 | 0.1586 | 1.0091 |
| 0.0669 | 10.56 | 58400 | 0.1603 | 1.0085 |
| 0.068 | 10.58 | 58500 | 0.1579 | 1.0031 |
| 0.068 | 10.6 | 58600 | 0.1591 | 1.0021 |
| 0.068 | 10.62 | 58700 | 0.1590 | 1.0163 |
| 0.068 | 10.64 | 58800 | 0.1584 | 1.0045 |
| 0.068 | 10.65 | 58900 | 0.1594 | 1.0158 |
| 0.0693 | 10.67 | 59000 | 0.1568 | 1.0052 |
| 0.0693 | 10.69 | 59100 | 0.1581 | 0.9955 |
| 0.0693 | 10.71 | 59200 | 0.1622 | 0.9917 |
| 0.0693 | 10.73 | 59300 | 0.1580 | 1.0018 |
| 0.0693 | 10.75 | 59400 | 0.1601 | 1.0077 |
| 0.0699 | 10.76 | 59500 | 0.1605 | 0.9997 |
| 0.0699 | 10.78 | 59600 | 0.1585 | 1.0009 |
| 0.0699 | 10.8 | 59700 | 0.1541 | 1.0058 |
| 0.0699 | 10.82 | 59800 | 0.1583 | 1.0026 |
| 0.0699 | 10.84 | 59900 | 0.1592 | 0.9992 |
| 0.0671 | 10.85 | 60000 | 0.1590 | 1.0004 |
| 0.0671 | 10.87 | 60100 | 0.1585 | 1.0060 |
| 0.0671 | 10.89 | 60200 | 0.1579 | 1.0063 |
| 0.0671 | 10.91 | 60300 | 0.1582 | 0.9949 |
| 0.0671 | 10.93 | 60400 | 0.1562 | 1.0004 |
| 0.0661 | 10.94 | 60500 | 0.1560 | 0.9950 |
| 0.0661 | 10.96 | 60600 | 0.1564 | 0.9990 |
| 0.0661 | 10.98 | 60700 | 0.1552 | 0.9982 |
| 0.0661 | 11.0 | 60800 | 0.1596 | 1.0018 |
| 0.0661 | 11.02 | 60900 | 0.1618 | 0.9905 |
| 0.0634 | 11.03 | 61000 | 0.1652 | 0.9890 |
| 0.0634 | 11.05 | 61100 | 0.1649 | 0.9886 |
| 0.0634 | 11.07 | 61200 | 0.1668 | 0.9870 |
| 0.0634 | 11.09 | 61300 | 0.1663 | 0.9921 |
| 0.0634 | 11.11 | 61400 | 0.1650 | 0.9919 |
| 0.0587 | 11.13 | 61500 | 0.1674 | 0.9831 |
| 0.0587 | 11.14 | 61600 | 0.1633 | 0.9793 |
| 0.0587 | 11.16 | 61700 | 0.1665 | 0.9781 |
| 0.0587 | 11.18 | 61800 | 0.1642 | 0.9821 |
| 0.0587 | 11.2 | 61900 | 0.1638 | 0.9797 |
| 0.0581 | 11.22 | 62000 | 0.1628 | 0.9727 |
| 0.0581 | 11.23 | 62100 | 0.1661 | 0.9796 |
| 0.0581 | 11.25 | 62200 | 0.1641 | 0.9830 |
| 0.0581 | 11.27 | 62300 | 0.1601 | 0.9867 |
| 0.0581 | 11.29 | 62400 | 0.1626 | 0.9757 |
| 0.0584 | 11.31 | 62500 | 0.1632 | 1.0014 |
| 0.0584 | 11.32 | 62600 | 0.1626 | 1.0052 |
| 0.0584 | 11.34 | 62700 | 0.1586 | 1.0098 |
| 0.0584 | 11.36 | 62800 | 0.1597 | 1.0151 |
| 0.0584 | 11.38 | 62900 | 0.1624 | 1.0054 |
| 0.0589 | 11.4 | 63000 | 0.1618 | 1.0018 |
| 0.0589 | 11.41 | 63100 | 0.1635 | 1.0032 |
| 0.0589 | 11.43 | 63200 | 0.1654 | 1.0142 |
| 0.0589 | 11.45 | 63300 | 0.1646 | 1.0031 |
| 0.0589 | 11.47 | 63400 | 0.1618 | 1.0118 |
| 0.0579 | 11.49 | 63500 | 0.1634 | 1.0218 |
| 0.0579 | 11.51 | 63600 | 0.1616 | 1.0179 |
| 0.0579 | 11.52 | 63700 | 0.1603 | 1.0036 |
| 0.0579 | 11.54 | 63800 | 0.1610 | 1.0150 |
| 0.0579 | 11.56 | 63900 | 0.1605 | 1.0285 |
| 0.0572 | 11.58 | 64000 | 0.1621 | 1.0261 |
| 0.0572 | 11.6 | 64100 | 0.1625 | 1.0252 |
| 0.0572 | 11.61 | 64200 | 0.1677 | 1.0257 |
| 0.0572 | 11.63 | 64300 | 0.1656 | 1.0243 |
| 0.0572 | 11.65 | 64400 | 0.1669 | 1.0270 |
| 0.0592 | 11.67 | 64500 | 0.1605 | 1.0305 |
| 0.0592 | 11.69 | 64600 | 0.1633 | 1.0277 |
| 0.0592 | 11.7 | 64700 | 0.1606 | 1.0176 |
| 0.0592 | 11.72 | 64800 | 0.1618 | 1.0249 |
| 0.0592 | 11.74 | 64900 | 0.1609 | 1.0113 |
| 0.0595 | 11.76 | 65000 | 0.1609 | 1.0254 |
| 0.0595 | 11.78 | 65100 | 0.1662 | 1.0275 |
| 0.0595 | 11.79 | 65200 | 0.1652 | 1.0164 |
| 0.0595 | 11.81 | 65300 | 0.1638 | 1.0266 |
| 0.0595 | 11.83 | 65400 | 0.1589 | 1.0274 |
| 0.0588 | 11.85 | 65500 | 0.1607 | 1.0136 |
| 0.0588 | 11.87 | 65600 | 0.1592 | 1.0136 |
| 0.0588 | 11.88 | 65700 | 0.1581 | 1.0183 |
| 0.0588 | 11.9 | 65800 | 0.1587 | 1.0133 |
| 0.0588 | 11.92 | 65900 | 0.1596 | 1.0170 |
| 0.0558 | 11.94 | 66000 | 0.1590 | 1.0161 |
| 0.0558 | 11.96 | 66100 | 0.1597 | 1.0193 |
| 0.0558 | 11.98 | 66200 | 0.1590 | 1.0193 |
| 0.0558 | 11.99 | 66300 | 0.1608 | 1.0242 |
| 0.0558 | 12.01 | 66400 | 0.1642 | 1.0231 |
| 0.0555 | 12.03 | 66500 | 0.1679 | 1.0168 |
| 0.0555 | 12.05 | 66600 | 0.1674 | 1.0083 |
| 0.0555 | 12.07 | 66700 | 0.1658 | 1.0069 |
| 0.0555 | 12.08 | 66800 | 0.1661 | 1.0134 |
| 0.0555 | 12.1 | 66900 | 0.1682 | 1.0274 |
| 0.0508 | 12.12 | 67000 | 0.1702 | 1.0219 |
| 0.0508 | 12.14 | 67100 | 0.1694 | 1.0219 |
| 0.0508 | 12.16 | 67200 | 0.1667 | 1.0236 |
| 0.0508 | 12.17 | 67300 | 0.1672 | 1.0253 |
| 0.0508 | 12.19 | 67400 | 0.1640 | 1.0215 |
| 0.0513 | 12.21 | 67500 | 0.1649 | 1.0242 |
| 0.0513 | 12.23 | 67600 | 0.1687 | 1.0262 |
| 0.0513 | 12.25 | 67700 | 0.1655 | 1.0231 |
| 0.0513 | 12.26 | 67800 | 0.1692 | 1.0176 |
| 0.0513 | 12.28 | 67900 | 0.1675 | 1.0202 |
| 0.0519 | 12.3 | 68000 | 0.1644 | 1.0241 |
| 0.0519 | 12.32 | 68100 | 0.1651 | 1.0297 |
| 0.0519 | 12.34 | 68200 | 0.1661 | 1.0287 |
| 0.0519 | 12.36 | 68300 | 0.1665 | 1.0257 |
| 0.0519 | 12.37 | 68400 | 0.1685 | 1.0233 |
| 0.0522 | 12.39 | 68500 | 0.1636 | 1.0177 |
| 0.0522 | 12.41 | 68600 | 0.1709 | 1.0200 |
| 0.0522 | 12.43 | 68700 | 0.1684 | 1.0164 |
| 0.0522 | 12.45 | 68800 | 0.1666 | 1.0119 |
| 0.0522 | 12.46 | 68900 | 0.1683 | 1.0136 |
| 0.05 | 12.48 | 69000 | 0.1696 | 1.0127 |
| 0.05 | 12.5 | 69100 | 0.1708 | 1.0184 |
| 0.05 | 12.52 | 69200 | 0.1654 | 1.0282 |
| 0.05 | 12.54 | 69300 | 0.1700 | 1.0235 |
| 0.05 | 12.55 | 69400 | 0.1688 | 1.0257 |
| 0.0513 | 12.57 | 69500 | 0.1646 | 1.0274 |
| 0.0513 | 12.59 | 69600 | 0.1660 | 1.0247 |
| 0.0513 | 12.61 | 69700 | 0.1657 | 1.0188 |
| 0.0513 | 12.63 | 69800 | 0.1654 | 1.0087 |
| 0.0513 | 12.64 | 69900 | 0.1681 | 1.0146 |
| 0.0512 | 12.66 | 70000 | 0.1660 | 1.0185 |
| 0.0512 | 12.68 | 70100 | 0.1690 | 1.0214 |
| 0.0512 | 12.7 | 70200 | 0.1683 | 1.0160 |
| 0.0512 | 12.72 | 70300 | 0.1695 | 1.0198 |
| 0.0512 | 12.74 | 70400 | 0.1666 | 1.0193 |
| 0.0484 | 12.75 | 70500 | 0.1654 | 1.0142 |
| 0.0484 | 12.77 | 70600 | 0.1598 | 1.0154 |
| 0.0484 | 12.79 | 70700 | 0.1623 | 1.0139 |
| 0.0484 | 12.81 | 70800 | 0.1662 | 1.0180 |
| 0.0484 | 12.83 | 70900 | 0.1659 | 1.0232 |
| 0.0501 | 12.84 | 71000 | 0.1662 | 1.0202 |
| 0.0501 | 12.86 | 71100 | 0.1639 | 1.0161 |
| 0.0501 | 12.88 | 71200 | 0.1666 | 1.0151 |
| 0.0501 | 12.9 | 71300 | 0.1644 | 1.0129 |
| 0.0501 | 12.92 | 71400 | 0.1642 | 1.0171 |
| 0.0482 | 12.93 | 71500 | 0.1635 | 1.0162 |
| 0.0482 | 12.95 | 71600 | 0.1637 | 1.0186 |
| 0.0482 | 12.97 | 71700 | 0.1639 | 1.0142 |
| 0.0482 | 12.99 | 71800 | 0.1643 | 1.0122 |
| 0.0482 | 13.01 | 71900 | 0.1679 | 1.0156 |
| 0.0483 | 13.02 | 72000 | 0.1717 | 1.0224 |
| 0.0483 | 13.04 | 72100 | 0.1742 | 1.0229 |
| 0.0483 | 13.06 | 72200 | 0.1718 | 1.0237 |
| 0.0483 | 13.08 | 72300 | 0.1742 | 1.0266 |
| 0.0483 | 13.1 | 72400 | 0.1736 | 1.0257 |
| 0.0443 | 13.12 | 72500 | 0.1741 | 1.0275 |
| 0.0443 | 13.13 | 72600 | 0.1745 | 1.0325 |
| 0.0443 | 13.15 | 72700 | 0.1737 | 1.0296 |
| 0.0443 | 13.17 | 72800 | 0.1722 | 1.0303 |
| 0.0443 | 13.19 | 72900 | 0.1702 | 1.0305 |
| 0.0424 | 13.21 | 73000 | 0.1733 | 1.0241 |
| 0.0424 | 13.22 | 73100 | 0.1748 | 1.0243 |
| 0.0424 | 13.24 | 73200 | 0.1760 | 1.0231 |
| 0.0424 | 13.26 | 73300 | 0.1745 | 1.0241 |
| 0.0424 | 13.28 | 73400 | 0.1772 | 1.0217 |
| 0.0424 | 13.3 | 73500 | 0.1755 | 1.0206 |
| 0.0424 | 13.31 | 73600 | 0.1743 | 1.0242 |
| 0.0424 | 13.33 | 73700 | 0.1738 | 1.0208 |
| 0.0424 | 13.35 | 73800 | 0.1736 | 1.0249 |
| 0.0424 | 13.37 | 73900 | 0.1747 | 1.0271 |
| 0.0437 | 13.39 | 74000 | 0.1707 | 1.0241 |
| 0.0437 | 13.4 | 74100 | 0.1731 | 1.0269 |
| 0.0437 | 13.42 | 74200 | 0.1743 | 1.0290 |
| 0.0437 | 13.44 | 74300 | 0.1739 | 1.0266 |
| 0.0437 | 13.46 | 74400 | 0.1763 | 1.0246 |
| 0.0443 | 13.48 | 74500 | 0.1724 | 1.0209 |
| 0.0443 | 13.49 | 74600 | 0.1744 | 1.0244 |
| 0.0443 | 13.51 | 74700 | 0.1717 | 1.0232 |
| 0.0443 | 13.53 | 74800 | 0.1754 | 1.0217 |
| 0.0443 | 13.55 | 74900 | 0.1721 | 1.0234 |
| 0.0435 | 13.57 | 75000 | 0.1751 | 1.0197 |
| 0.0435 | 13.59 | 75100 | 0.1727 | 1.0285 |
| 0.0435 | 13.6 | 75200 | 0.1715 | 1.0221 |
| 0.0435 | 13.62 | 75300 | 0.1746 | 1.0247 |
| 0.0435 | 13.64 | 75400 | 0.1712 | 1.0231 |
| 0.0436 | 13.66 | 75500 | 0.1719 | 1.0228 |
| 0.0436 | 13.68 | 75600 | 0.1727 | 1.0197 |
| 0.0436 | 13.69 | 75700 | 0.1750 | 1.0252 |
| 0.0436 | 13.71 | 75800 | 0.1702 | 1.0241 |
| 0.0436 | 13.73 | 75900 | 0.1720 | 1.0250 |
| 0.0433 | 13.75 | 76000 | 0.1744 | 1.0210 |
| 0.0433 | 13.77 | 76100 | 0.1735 | 1.0211 |
| 0.0433 | 13.78 | 76200 | 0.1727 | 1.0205 |
| 0.0433 | 13.8 | 76300 | 0.1706 | 1.0218 |
| 0.0433 | 13.82 | 76400 | 0.1709 | 1.0238 |
| 0.0431 | 13.84 | 76500 | 0.1705 | 1.0197 |
| 0.0431 | 13.86 | 76600 | 0.1734 | 1.0223 |
| 0.0431 | 13.87 | 76700 | 0.1695 | 1.0250 |
| 0.0431 | 13.89 | 76800 | 0.1734 | 1.0232 |
| 0.0431 | 13.91 | 76900 | 0.1724 | 1.0219 |
| 0.041 | 13.93 | 77000 | 0.1706 | 1.0236 |
| 0.041 | 13.95 | 77100 | 0.1689 | 1.0220 |
| 0.041 | 13.97 | 77200 | 0.1738 | 1.0230 |
| 0.041 | 13.98 | 77300 | 0.1727 | 1.0254 |
| 0.041 | 14.0 | 77400 | 0.1721 | 1.0261 |
| 0.041 | 14.02 | 77500 | 0.1760 | 1.0261 |
| 0.041 | 14.04 | 77600 | 0.1772 | 1.0202 |
| 0.041 | 14.06 | 77700 | 0.1782 | 1.0202 |
| 0.041 | 14.07 | 77800 | 0.1777 | 1.0222 |
| 0.041 | 14.09 | 77900 | 0.1787 | 1.0203 |
| 0.0383 | 14.11 | 78000 | 0.1790 | 1.0236 |
| 0.0383 | 14.13 | 78100 | 0.1812 | 1.0245 |
| 0.0383 | 14.15 | 78200 | 0.1778 | 1.0224 |
| 0.0383 | 14.16 | 78300 | 0.1771 | 1.0231 |
| 0.0383 | 14.18 | 78400 | 0.1782 | 1.0242 |
| 0.0391 | 14.2 | 78500 | 0.1785 | 1.0262 |
| 0.0391 | 14.22 | 78600 | 0.1791 | 1.0261 |
| 0.0391 | 14.24 | 78700 | 0.1770 | 1.0254 |
| 0.0391 | 14.25 | 78800 | 0.1810 | 1.0257 |
| 0.0391 | 14.27 | 78900 | 0.1794 | 1.0241 |
| 0.0387 | 14.29 | 79000 | 0.1774 | 1.0256 |
| 0.0387 | 14.31 | 79100 | 0.1774 | 1.0236 |
| 0.0387 | 14.33 | 79200 | 0.1759 | 1.0222 |
| 0.0387 | 14.35 | 79300 | 0.1787 | 1.0237 |
| 0.0387 | 14.36 | 79400 | 0.1788 | 1.0227 |
| 0.0372 | 14.38 | 79500 | 0.1789 | 1.0232 |
| 0.0372 | 14.4 | 79600 | 0.1771 | 1.0254 |
| 0.0372 | 14.42 | 79700 | 0.1777 | 1.0244 |
| 0.0372 | 14.44 | 79800 | 0.1791 | 1.0225 |
| 0.0372 | 14.45 | 79900 | 0.1786 | 1.0237 |
| 0.0385 | 14.47 | 80000 | 0.1782 | 1.0243 |
| 0.0385 | 14.49 | 80100 | 0.1770 | 1.0236 |
| 0.0385 | 14.51 | 80200 | 0.1782 | 1.0240 |
| 0.0385 | 14.53 | 80300 | 0.1764 | 1.0243 |
| 0.0385 | 14.54 | 80400 | 0.1748 | 1.0248 |
| 0.039 | 14.56 | 80500 | 0.1758 | 1.0232 |
| 0.039 | 14.58 | 80600 | 0.1763 | 1.0246 |
| 0.039 | 14.6 | 80700 | 0.1770 | 1.0220 |
| 0.039 | 14.62 | 80800 | 0.1788 | 1.0225 |
| 0.039 | 14.63 | 80900 | 0.1781 | 1.0230 |
| 0.039 | 14.65 | 81000 | 0.1779 | 1.0230 |
| 0.039 | 14.67 | 81100 | 0.1755 | 1.0212 |
| 0.039 | 14.69 | 81200 | 0.1765 | 1.0226 |
| 0.039 | 14.71 | 81300 | 0.1787 | 1.0241 |
| 0.039 | 14.72 | 81400 | 0.1782 | 1.0250 |
| 0.0368 | 14.74 | 81500 | 0.1780 | 1.0248 |
| 0.0368 | 14.76 | 81600 | 0.1782 | 1.0242 |
| 0.0368 | 14.78 | 81700 | 0.1782 | 1.0242 |
| 0.0368 | 14.8 | 81800 | 0.1792 | 1.0241 |
| 0.0368 | 14.82 | 81900 | 0.1796 | 1.0238 |
| 0.0378 | 14.83 | 82000 | 0.1795 | 1.0236 |
| 0.0378 | 14.85 | 82100 | 0.1796 | 1.0239 |
| 0.0378 | 14.87 | 82200 | 0.1792 | 1.0236 |
| 0.0378 | 14.89 | 82300 | 0.1789 | 1.0239 |
| 0.0378 | 14.91 | 82400 | 0.1788 | 1.0238 |
| 0.0386 | 14.92 | 82500 | 0.1787 | 1.0239 |
| 0.0386 | 14.94 | 82600 | 0.1786 | 1.0236 |
| 0.0386 | 14.96 | 82700 | 0.1786 | 1.0237 |
| 0.0386 | 14.98 | 82800 | 0.1787 | 1.0239 |
| 0.0386 | 15.0 | 82900 | 0.1788 | 1.0238 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-es-demo", "results": []}]}
|
automatic-speech-recognition
|
gabrieljg/wav2vec2-common_voice-es-demo
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-common\_voice-es-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - ES dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1788
* Wer: 1.0239
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
69,
159,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium).
This model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
|
{"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"]}
|
text-generation
|
gabtan99/dialogpt-tagalog-medium-10
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tl"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (URL
This model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
|
[
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
[
61,
65
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
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] |
null | null |
transformers
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium).
This model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
|
{"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"], "inference": false}
|
text-generation
|
gabtan99/dialogpt-tagalog-medium-20
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tl"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (URL
This model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
|
[
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
[
53,
65
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us \n# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
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] |
null | null |
transformers
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium).
This model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
|
{"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"], "inference": false}
|
text-generation
|
gabtan99/dialogpt-tagalog-medium-30
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tl"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us
|
# Tagalog DialoGPT
This is an extension of the base Tagalog DialoGPT model (URL
This model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
|
[
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
[
53,
65
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #autotrain_compatible #text-generation-inference #region-us \n# Tagalog DialoGPT\n\nThis is an extension of the base Tagalog DialoGPT model (URL \n\nThis model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens."
] |
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] |
null | null |
transformers
|
# Tagalog DialoGPT
A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.
# Latest release: July 25, 2021
* The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset.
# Dataset
[PEx Conversations Dataset](https://huggingface.co/datasets/gabtan99/pex-conversations)
# Usage
Here is an example of using beam search for model inference.
```
for step in range(2):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# we limit the generation to 512 tokens, each utterance in training had a maximum of 128 tokens
chat_history_ids = model.generate(
bot_input_ids, max_length=512,
pad_token_id=tokenizer.eos_token_id,
num_beams=5,
no_repeat_ngram_size=3
)
# pretty print last ouput tokens from bot
print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
# Training Script
[Fine-tuning script adapted from Spanish DialoGPT](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)
# Research by
* [tyadrianpaule](https://huggingface.co/tyadrianpaule)
* [schuylerng](https://huggingface.co/schuylerng)
* [dcl127](https://huggingface.co/dcl127)
|
{"language": ["tl"], "tags": ["conversational", "tagalog", "filipino"], "datasets": ["gabtan99/pex-conversations"], "inference": false}
|
text-generation
|
gabtan99/dialogpt-tagalog-medium
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"tagalog",
"filipino",
"tl",
"dataset:gabtan99/pex-conversations",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tl"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #dataset-gabtan99/pex-conversations #autotrain_compatible #has_space #text-generation-inference #region-us
|
# Tagalog DialoGPT
A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.
# Latest release: July 25, 2021
* The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset.
# Dataset
PEx Conversations Dataset
# Usage
Here is an example of using beam search for model inference.
# Training Script
Fine-tuning script adapted from Spanish DialoGPT
# Research by
* tyadrianpaule
* schuylerng
* dcl127
|
[
"# Tagalog DialoGPT\nA DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.",
"# Latest release: July 25, 2021\n* The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset.",
"# Dataset\nPEx Conversations Dataset",
"# Usage\nHere is an example of using beam search for model inference.",
"# Training Script\nFine-tuning script adapted from Spanish DialoGPT",
"# Research by\n* tyadrianpaule\n* schuylerng\n* dcl127"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #dataset-gabtan99/pex-conversations #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# Tagalog DialoGPT\nA DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.",
"# Latest release: July 25, 2021\n* The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset.",
"# Dataset\nPEx Conversations Dataset",
"# Usage\nHere is an example of using beam search for model inference.",
"# Training Script\nFine-tuning script adapted from Spanish DialoGPT",
"# Research by\n* tyadrianpaule\n* schuylerng\n* dcl127"
] |
[
71,
71,
49,
10,
17,
16,
19
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #tagalog #filipino #tl #dataset-gabtan99/pex-conversations #autotrain_compatible #has_space #text-generation-inference #region-us \n# Tagalog DialoGPT\nA DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training.# Latest release: July 25, 2021\n* The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset.# Dataset\nPEx Conversations Dataset# Usage\nHere is an example of using beam search for model inference.# Training Script\nFine-tuning script adapted from Spanish DialoGPT# Research by\n* tyadrianpaule\n* schuylerng\n* dcl127"
] |
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] |
null | null | null |
I am adding my first README in order to test the interface. How good is it really?
|
{}
| null |
gael1130/gael_first_model
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
I am adding my first README in order to test the interface. How good is it really?
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
{"license": "apache-2.0"}
|
text2text-generation
|
gaetangate/bart-large_genrl_lcquad1
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2108.07337"
] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
{"license": "apache-2.0"}
|
text2text-generation
|
gaetangate/bart-large_genrl_lcquad2
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2108.07337"
] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
{"license": "apache-2.0"}
|
text2text-generation
|
gaetangate/bart-large_genrl_qald9
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2108.07337"
] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
{"license": "apache-2.0"}
|
text2text-generation
|
gaetangate/bart-large_genrl_simpleq
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2108.07337"
] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null | null |
test 123
|
{}
| null |
gaga42gaga42/test
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
test 123
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
# Generating Right Wing News Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data from FOX news
### My model can be accessed at gagan3012/Fox-News-Generator
Check the [BenchmarkTest](https://github.com/gagan3012/Fox-News-Generator/blob/master/BenchmarkTest.ipynb) notebook for results
Find the model at [gagan3012/Fox-News-Generator](https://huggingface.co/gagan3012/Fox-News-Generator)
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/Fox-News-Generator")
model = AutoModelWithLMHead.from_pretrained("gagan3012/Fox-News-Generator")
```
|
{}
|
text-generation
|
gagan3012/Fox-News-Generator
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Generating Right Wing News Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data from FOX news
### My model can be accessed at gagan3012/Fox-News-Generator
Check the BenchmarkTest notebook for results
Find the model at gagan3012/Fox-News-Generator
|
[
"# Generating Right Wing News Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data from FOX news",
"### My model can be accessed at gagan3012/Fox-News-Generator\n\nCheck the BenchmarkTest notebook for results\n\nFind the model at gagan3012/Fox-News-Generator"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Generating Right Wing News Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data from FOX news",
"### My model can be accessed at gagan3012/Fox-News-Generator\n\nCheck the BenchmarkTest notebook for results\n\nFind the model at gagan3012/Fox-News-Generator"
] |
[
50,
12,
29,
46
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Generating Right Wing News Using GPT2### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data from FOX news### My model can be accessed at gagan3012/Fox-News-Generator\n\nCheck the BenchmarkTest notebook for results\n\nFind the model at gagan3012/Fox-News-Generator"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViTGPT2I2A
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the vizwiz dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1528 | 0.17 | 1000 | 0.0869 |
| 0.0899 | 0.34 | 2000 | 0.0817 |
| 0.084 | 0.51 | 3000 | 0.0790 |
| 0.0814 | 0.68 | 4000 | 0.0773 |
| 0.0803 | 0.85 | 5000 | 0.0757 |
| 0.077 | 1.02 | 6000 | 0.0745 |
| 0.0739 | 1.19 | 7000 | 0.0740 |
| 0.0719 | 1.37 | 8000 | 0.0737 |
| 0.0717 | 1.54 | 9000 | 0.0730 |
| 0.0731 | 1.71 | 10000 | 0.0727 |
| 0.0708 | 1.88 | 11000 | 0.0720 |
| 0.0697 | 2.05 | 12000 | 0.0717 |
| 0.0655 | 2.22 | 13000 | 0.0719 |
| 0.0653 | 2.39 | 14000 | 0.0719 |
| 0.0657 | 2.56 | 15000 | 0.0712 |
| 0.0663 | 2.73 | 16000 | 0.0710 |
| 0.0654 | 2.9 | 17000 | 0.0708 |
| 0.0645 | 3.07 | 18000 | 0.0716 |
| 0.0616 | 3.24 | 19000 | 0.0712 |
| 0.0607 | 3.41 | 20000 | 0.0712 |
| 0.0611 | 3.58 | 21000 | 0.0711 |
| 0.0615 | 3.76 | 22000 | 0.0711 |
| 0.0614 | 3.93 | 23000 | 0.0710 |
| 0.0594 | 4.1 | 24000 | 0.0716 |
| 0.0587 | 4.27 | 25000 | 0.0715 |
| 0.0574 | 4.44 | 26000 | 0.0715 |
| 0.0579 | 4.61 | 27000 | 0.0715 |
| 0.0581 | 4.78 | 28000 | 0.0715 |
| 0.0579 | 4.95 | 29000 | 0.0715 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["image-captioning", "generated_from_trainer"], "model-index": [{"name": "ViTGPT2I2A", "results": []}]}
| null |
gagan3012/ViTGPT2I2A
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-captioning",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #vision-encoder-decoder #image-captioning #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
ViTGPT2I2A
==========
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the vizwiz dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0708
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 4
* total\_eval\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-captioning #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
54,
162,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #vision-encoder-decoder #image-captioning #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViTGPT2_VW
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0771
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1256 | 0.03 | 1000 | 0.0928 |
| 0.0947 | 0.07 | 2000 | 0.0897 |
| 0.0889 | 0.1 | 3000 | 0.0859 |
| 0.0888 | 0.14 | 4000 | 0.0842 |
| 0.0866 | 0.17 | 5000 | 0.0831 |
| 0.0852 | 0.2 | 6000 | 0.0819 |
| 0.0833 | 0.24 | 7000 | 0.0810 |
| 0.0835 | 0.27 | 8000 | 0.0802 |
| 0.081 | 0.31 | 9000 | 0.0796 |
| 0.0803 | 0.34 | 10000 | 0.0789 |
| 0.0814 | 0.38 | 11000 | 0.0785 |
| 0.0799 | 0.41 | 12000 | 0.0780 |
| 0.0786 | 0.44 | 13000 | 0.0776 |
| 0.0796 | 0.48 | 14000 | 0.0771 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "ViTGPT2_VW", "results": []}]}
| null |
gagan3012/ViTGPT2_VW
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #endpoints_compatible #region-us
|
ViTGPT2\_VW
===========
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0771
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 4
* total\_eval\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.2+cu113
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
36,
162,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 4\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViTGPT2_vizwiz
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0719
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1207 | 0.07 | 1000 | 0.0906 |
| 0.0916 | 0.14 | 2000 | 0.0861 |
| 0.0879 | 0.2 | 3000 | 0.0840 |
| 0.0856 | 0.27 | 4000 | 0.0822 |
| 0.0834 | 0.34 | 5000 | 0.0806 |
| 0.0817 | 0.41 | 6000 | 0.0795 |
| 0.0812 | 0.48 | 7000 | 0.0785 |
| 0.0808 | 0.55 | 8000 | 0.0779 |
| 0.0796 | 0.61 | 9000 | 0.0771 |
| 0.0786 | 0.68 | 10000 | 0.0767 |
| 0.0774 | 0.75 | 11000 | 0.0762 |
| 0.0772 | 0.82 | 12000 | 0.0758 |
| 0.0756 | 0.89 | 13000 | 0.0754 |
| 0.0759 | 0.96 | 14000 | 0.0750 |
| 0.0756 | 1.02 | 15000 | 0.0748 |
| 0.0726 | 1.09 | 16000 | 0.0745 |
| 0.0727 | 1.16 | 17000 | 0.0745 |
| 0.0715 | 1.23 | 18000 | 0.0742 |
| 0.0726 | 1.3 | 19000 | 0.0741 |
| 0.072 | 1.37 | 20000 | 0.0738 |
| 0.0723 | 1.43 | 21000 | 0.0735 |
| 0.0715 | 1.5 | 22000 | 0.0734 |
| 0.0724 | 1.57 | 23000 | 0.0732 |
| 0.0723 | 1.64 | 24000 | 0.0730 |
| 0.0718 | 1.71 | 25000 | 0.0729 |
| 0.07 | 1.78 | 26000 | 0.0728 |
| 0.0702 | 1.84 | 27000 | 0.0726 |
| 0.0704 | 1.91 | 28000 | 0.0725 |
| 0.0703 | 1.98 | 29000 | 0.0725 |
| 0.0686 | 2.05 | 30000 | 0.0726 |
| 0.0687 | 2.12 | 31000 | 0.0726 |
| 0.0688 | 2.19 | 32000 | 0.0724 |
| 0.0677 | 2.25 | 33000 | 0.0724 |
| 0.0665 | 2.32 | 34000 | 0.0725 |
| 0.0684 | 2.39 | 35000 | 0.0723 |
| 0.0678 | 2.46 | 36000 | 0.0722 |
| 0.0686 | 2.53 | 37000 | 0.0722 |
| 0.067 | 2.59 | 38000 | 0.0721 |
| 0.0669 | 2.66 | 39000 | 0.0721 |
| 0.0673 | 2.73 | 40000 | 0.0721 |
| 0.0673 | 2.8 | 41000 | 0.0720 |
| 0.0662 | 2.87 | 42000 | 0.0720 |
| 0.0681 | 2.94 | 43000 | 0.0719 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
{"tags": ["generated_from_trainer", "image-to-text"], "model-index": [{"name": "ViTGPT2_vizwiz", "results": []}]}
|
image-to-text
|
gagan3012/ViTGPT2_vizwiz
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"generated_from_trainer",
"image-to-text",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #endpoints_compatible #has_space #region-us
|
ViTGPT2\_vizwiz
===============
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0719
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
[
46,
124,
4,
39
] |
[
"passage: TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-finetuned-ner
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1689
- Precision: 0.8083
- Recall: 0.8274
- F1: 0.8177
- Accuracy: 0.9598
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0355 | 1.0 | 878 | 0.1692 | 0.8072 | 0.8248 | 0.8159 | 0.9594 |
| 0.0411 | 2.0 | 1756 | 0.1678 | 0.8101 | 0.8277 | 0.8188 | 0.9600 |
| 0.0386 | 3.0 | 2634 | 0.1697 | 0.8103 | 0.8269 | 0.8186 | 0.9599 |
| 0.0373 | 4.0 | 3512 | 0.1694 | 0.8106 | 0.8263 | 0.8183 | 0.9600 |
| 0.0383 | 5.0 | 4390 | 0.1689 | 0.8083 | 0.8274 | 0.8177 | 0.9598 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-tiny-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.8083060109289617, "name": "Precision"}, {"type": "recall", "value": 0.8273856136033113, "name": "Recall"}, {"type": "f1", "value": 0.8177345348001547, "name": "F1"}, {"type": "accuracy", "value": 0.9597597979252387, "name": "Accuracy"}]}]}]}
|
token-classification
|
gagan3012/bert-tiny-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
bert-tiny-finetuned-ner
=======================
This model is a fine-tuned version of prajjwal1/bert-tiny on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1689
* Precision: 0.8083
* Recall: 0.8274
* F1: 0.8177
* Accuracy: 0.9598
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.10.0
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
63,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9274
- Recall: 0.9363
- F1: 0.9319
- Accuracy: 0.9840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2403 | 1.0 | 878 | 0.0701 | 0.9101 | 0.9202 | 0.9151 | 0.9805 |
| 0.0508 | 2.0 | 1756 | 0.0600 | 0.9220 | 0.9350 | 0.9285 | 0.9833 |
| 0.0301 | 3.0 | 2634 | 0.0614 | 0.9274 | 0.9363 | 0.9319 | 0.9840 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9274238227146815, "name": "Precision"}, {"type": "recall", "value": 0.9363463474661595, "name": "Recall"}, {"type": "f1", "value": 0.9318637274549098, "name": "F1"}, {"type": "accuracy", "value": 0.9839865283492462, "name": "Accuracy"}]}]}]}
|
token-classification
|
gagan3012/distilbert-base-uncased-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9274
* Recall: 0.9363
* F1: 0.9319
* Accuracy: 0.9840
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3"
] |
[
69,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
## Model:
Keytotext is based on the Amazing T5 Model:
- `k2t`: [Model](https://huggingface.co/gagan3012/k2t)
- `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny)
- `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base)
Training Notebooks can be found in the [`Training Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Training%20Notebooks) Folder
## Usage:
Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder
```
pip install keytotext
```
## UI:
UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
```
pip install streamlit-tags
```
This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)
|
{"language": "en", "license": "mit", "tags": ["keytotext", "k2t-base", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"}
|
text2text-generation
|
gagan3012/k2t-base
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t-base",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-base #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
## UI:
UI: ![Streamlit App](URL
This uses a custom streamlit component built by me: GitHub
!image
|
[
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-base #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
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[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-base #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.### Keytotext is powered by Huggingface \n\n## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
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] |
null | null |
transformers
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
## Model:
Keytotext is based on the Amazing T5 Model:
- `k2t`: [Model](https://huggingface.co/gagan3012/k2t)
- `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny)
- `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base)
Training Notebooks can be found in the [`Training Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Training%20Notebooks) Folder
## Usage:
Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder
```
pip install keytotext
```
## UI:
UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
```
pip install streamlit-tags
```
This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)
|
{"language": "en", "license": "mit", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["common_gen"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"}
|
text2text-generation
|
gagan3012/k2t-new
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:common_gen",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-common_gen #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
## UI:
UI: ![Streamlit App](URL
This uses a custom streamlit component built by me: GitHub
!image
|
[
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-common_gen #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
81,
31,
44,
59,
36,
30
] |
[
"passage: TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-common_gen #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.### Keytotext is powered by Huggingface \n\n## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
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] |
null | null |
transformers
|
<h1 align="center">keytotext</h1>
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/notebooks/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
[](https://github.com/gagan3012/keytotext#api)
[](https://hub.docker.com/r/gagan30/keytotext)
[](https://huggingface.co/models?filter=keytotext)
[](https://keytotext.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models
|
{"language": "en", "license": "MIT", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"}
|
text2text-generation
|
gagan3012/k2t-test
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<h1 align="center">keytotext</h1>
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
![API Call](URL
![Docker Call](URL
![HuggingFace](URL
![Documentation Status](URL
![Code style: black](URL
!keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
78
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
#keytotext
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/notebooks/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
[](https://github.com/gagan3012/keytotext#api)
[](https://hub.docker.com/r/gagan30/keytotext)
[](https://huggingface.co/models?filter=keytotext)
[](https://keytotext.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models
|
{"language": "en", "license": "MIT", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"}
|
text2text-generation
|
gagan3012/k2t-test3
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#keytotext
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
![API Call](URL
![Docker Call](URL
![HuggingFace](URL
![Documentation Status](URL
![Code style: black](URL
!keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
78
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
## Model:
Keytotext is based on the Amazing T5 Model:
- `k2t`: [Model](https://huggingface.co/gagan3012/k2t)
- `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny)
- `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base)
Training Notebooks can be found in the [`Training Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Training%20Notebooks) Folder
## Usage:
Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder
```
pip install keytotext
```
## UI:
UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
```
pip install streamlit-tags
```
This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)
|
{"language": "en", "license": "mit", "tags": ["keytotext", "k2t-tiny", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"}
|
text2text-generation
|
gagan3012/k2t-tiny
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t-tiny",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-tiny #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
## UI:
UI: ![Streamlit App](URL
This uses a custom streamlit component built by me: GitHub
!image
|
[
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-tiny #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
85,
31,
44,
59,
36,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t-tiny #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.### Keytotext is powered by Huggingface \n\n## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
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] |
null | null |
transformers
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
## Model:
Keytotext is based on the Amazing T5 Model:
- `k2t`: [Model](https://huggingface.co/gagan3012/k2t)
- `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny)
- `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base)
Training Notebooks can be found in the [`Training Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Training%20Notebooks) Folder
## Usage:
Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder
```
pip install keytotext
```
## UI:
UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
```
pip install streamlit-tags
```
This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)
|
{"language": "en", "license": "mit", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"}
|
text2text-generation
|
gagan3012/k2t
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
## UI:
UI: ![Streamlit App](URL
This uses a custom streamlit component built by me: GitHub
!image
|
[
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n\n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\n\nKeytotext is based on the Amazing T5 Model: \n\n- 'k2t': Model\n- 'k2t-tiny': Model\n- 'k2t-base': Model\n\nTraining Notebooks can be found in the 'Training Notebooks' Folder",
"## Usage:\n\nExample usage: ",
"## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
[
87,
31,
44,
59,
36,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# keytotext\n\n!keytotext (1)\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.### Keytotext is powered by Huggingface \n\n## UI:\n\nUI: ![Streamlit App](URL\n\n\nThis uses a custom streamlit component built by me: GitHub\n\n!image"
] |
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] |
null | null |
transformers
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: https://huggingface.co/gagan3012/keytotext
- Using T5-small size = 230 MB can be found here: https://huggingface.co/gagan3012/keytotext-small
#### Usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/keytotext-small")
model = AutoModelWithLMHead.from_pretrained("gagan3012/keytotext-small")
```
### Demo:
[](https://share.streamlit.io/gagan3012/keytotext/app.py)
https://share.streamlit.io/gagan3012/keytotext/app.py
### Example:
['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
|
{}
|
text2text-generation
|
gagan3012/keytotext-small
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: URL
- Using T5-small size = 230 MB can be found here: URL
#### Usage:
### Demo:
![Streamlit App](URL
URL
!image
### Example:
['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
|
[
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL",
"#### Usage:",
"### Demo:\n\n![Streamlit App](URL\n\nURL\n\n!image",
"### Example: \n\n['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL",
"#### Usage:",
"### Demo:\n\n![Streamlit App](URL\n\nURL\n\n!image",
"### Example: \n\n['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties."
] |
[
48,
26,
44,
5,
15,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL#### Usage:### Demo:\n\n![Streamlit App](URL\n\nURL\n\n!image### Example: \n\n['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties."
] |
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] |
null | null |
transformers
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: https://huggingface.co/gagan3012/keytotext
- Using T5-small size = 230 MB can be found here: https://huggingface.co/gagan3012/keytotext-small
#### Usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/keytotext-small")
model = AutoModelWithLMHead.from_pretrained("gagan3012/keytotext-small")
```
### Demo:
[](https://share.streamlit.io/gagan3012/keytotext/app.py)
https://share.streamlit.io/gagan3012/keytotext/app.py
### Example:
['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
|
{}
|
text2text-generation
|
gagan3012/keytotext
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Model:
Two Models have been built:
- Using T5-base size = 850 MB can be found here: URL
- Using T5-small size = 230 MB can be found here: URL
#### Usage:
### Demo:
![Streamlit App](URL
URL
!image
### Example:
['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties.
|
[
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL",
"#### Usage:",
"### Demo:\n\n![Streamlit App](URL\n\nURL\n\n!image",
"### Example: \n\n['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL",
"#### Usage:",
"### Demo:\n\n![Streamlit App](URL\n\nURL\n\n!image",
"### Example: \n\n['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties."
] |
[
48,
26,
44,
5,
15,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# keytotext\n\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.### Model:\n\nTwo Models have been built: \n\n- Using T5-base size = 850 MB can be found here: URL\n- Using T5-small size = 230 MB can be found here: URL#### Usage:### Demo:\n\n![Streamlit App](URL\n\nURL\n\n!image### Example: \n\n['India', 'Wedding'] -> We are celebrating today in New Delhi with three wedding anniversary parties."
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "model", "results": []}]}
|
text-generation
|
gagan3012/model
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# model
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.6250",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.6250",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
66,
43,
6,
12,
8,
3,
90,
4,
37
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.6250## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pickuplines
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "pickuplines", "results": []}]}
|
text-generation
|
gagan3012/pickuplines
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pickuplines
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# pickuplines\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.7873",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# pickuplines\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.7873",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
63,
43,
6,
12,
8,
3,
91,
4,
37
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# pickuplines\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.7873## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100.0### Training results### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
**Note**: the Answers might not make sense in some cases because of the bias in GPT-2
**Contribtuions:** If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code-py/blob/master/CONTRIBUTIONS.md)
### 📢 Favour:
It would be highly motivating, if you can STAR⭐ this repo if you find it helpful.
## Model
Two models have been developed for different use cases and they can be found at https://huggingface.co/gagan3012
The model weights can be found here: [GPT-2](https://huggingface.co/gagan3012/project-code-py) and [DistilGPT-2](https://huggingface.co/gagan3012/project-code-py-small)
### Example usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/project-code-py")
model = AutoModelWithLMHead.from_pretrained("gagan3012/project-code-py")
```
## Demo
[](https://share.streamlit.io/gagan3012/project-code-py/app.py)
A streamlit webapp has been setup to use the model: https://share.streamlit.io/gagan3012/project-code-py/app.py
## Example results:
### Question:
```
Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.
```
### Answer:
```python
""" Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.
For example,
a = 1->2->3
b = 3->1->2
t = ListNode(-1, 1)
Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree.
Example 1:
Input: [1,2,3]
Output: 1->2->5
Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4.
Note:
The length of a linked list will be in the range [1, 1000].
Node.val must be a valid LinkedListNode type.
Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000].
All nodes are distinct.
"""
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
def deleteNode(self, head: ListNode, val: int) -> None:
"""
BFS
Linked List
:param head: ListNode
:param val: int
:return: ListNode
"""
if head is not None:
return head
dummy = ListNode(-1, 1)
dummy.next = head
dummy.next.val = val
dummy.next.next = head
dummy.val = ""
s1 = Solution()
print(s1.deleteNode(head))
print(s1.deleteNode(-1))
print(s1.deleteNode(-1))
```
|
{}
|
text-generation
|
gagan3012/project-code-py-small
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
Note: the Answers might not make sense in some cases because of the bias in GPT-2
Contribtuions: If you would like to make the model better contributions are welcome Check out URL
### Favour:
It would be highly motivating, if you can STAR⭐ this repo if you find it helpful.
## Model
Two models have been developed for different use cases and they can be found at URL
The model weights can be found here: GPT-2 and DistilGPT-2
### Example usage:
## Demo
![Streamlit App](URL
A streamlit webapp has been setup to use the model: URL
!image
## Example results:
### Question:
### Answer:
|
[
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL",
"### Favour:\n\nIt would be highly motivating, if you can STAR⭐ this repo if you find it helpful.",
"## Model\n\nTwo models have been developed for different use cases and they can be found at URL\n\nThe model weights can be found here: GPT-2 and DistilGPT-2",
"### Example usage:",
"## Demo\n![Streamlit App](URL\n\n\nA streamlit webapp has been setup to use the model: URL\n\n!image",
"## Example results:",
"### Question:",
"### Answer:"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL",
"### Favour:\n\nIt would be highly motivating, if you can STAR⭐ this repo if you find it helpful.",
"## Model\n\nTwo models have been developed for different use cases and they can be found at URL\n\nThe model weights can be found here: GPT-2 and DistilGPT-2",
"### Example usage:",
"## Demo\n![Streamlit App](URL\n\n\nA streamlit webapp has been setup to use the model: URL\n\n!image",
"## Example results:",
"### Question:",
"### Answer:"
] |
[
54,
64,
25,
36,
6,
26,
5,
4,
4
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL### Favour:\n\nIt would be highly motivating, if you can STAR⭐ this repo if you find it helpful.## Model\n\nTwo models have been developed for different use cases and they can be found at URL\n\nThe model weights can be found here: GPT-2 and DistilGPT-2### Example usage:## Demo\n![Streamlit App](URL\n\n\nA streamlit webapp has been setup to use the model: URL\n\n!image## Example results:### Question:### Answer:"
] |
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null | null |
transformers
|
# Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
**Note**: the Answers might not make sense in some cases because of the bias in GPT-2
**Contribtuions:** If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code-py/blob/master/CONTRIBUTIONS.md)
### 📢 Favour:
It would be highly motivating, if you can STAR⭐ this repo if you find it helpful.
## Model
Two models have been developed for different use cases and they can be found at https://huggingface.co/gagan3012
The model weights can be found here: [GPT-2](https://huggingface.co/gagan3012/project-code-py) and [DistilGPT-2](https://huggingface.co/gagan3012/project-code-py-small)
### Example usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/project-code-py")
model = AutoModelWithLMHead.from_pretrained("gagan3012/project-code-py")
```
## Demo
[](https://share.streamlit.io/gagan3012/project-code-py/app.py)
A streamlit webapp has been setup to use the model: https://share.streamlit.io/gagan3012/project-code-py/app.py
## Example results:
### Question:
```
Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.
```
### Answer:
```python
""" Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.
For example,
a = 1->2->3
b = 3->1->2
t = ListNode(-1, 1)
Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree.
Example 1:
Input: [1,2,3]
Output: 1->2->5
Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4.
Note:
The length of a linked list will be in the range [1, 1000].
Node.val must be a valid LinkedListNode type.
Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000].
All nodes are distinct.
"""
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
def deleteNode(self, head: ListNode, val: int) -> None:
"""
BFS
Linked List
:param head: ListNode
:param val: int
:return: ListNode
"""
if head is not None:
return head
dummy = ListNode(-1, 1)
dummy.next = head
dummy.next.val = val
dummy.next.next = head
dummy.val = ""
s1 = Solution()
print(s1.deleteNode(head))
print(s1.deleteNode(-1))
print(s1.deleteNode(-1))
```
|
{}
|
text-generation
|
gagan3012/project-code-py
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
Note: the Answers might not make sense in some cases because of the bias in GPT-2
Contribtuions: If you would like to make the model better contributions are welcome Check out URL
### Favour:
It would be highly motivating, if you can STAR⭐ this repo if you find it helpful.
## Model
Two models have been developed for different use cases and they can be found at URL
The model weights can be found here: GPT-2 and DistilGPT-2
### Example usage:
## Demo
![Streamlit App](URL
A streamlit webapp has been setup to use the model: URL
!image
## Example results:
### Question:
### Answer:
|
[
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL",
"### Favour:\n\nIt would be highly motivating, if you can STAR⭐ this repo if you find it helpful.",
"## Model\n\nTwo models have been developed for different use cases and they can be found at URL\n\nThe model weights can be found here: GPT-2 and DistilGPT-2",
"### Example usage:",
"## Demo\n![Streamlit App](URL\n\n\nA streamlit webapp has been setup to use the model: URL\n\n!image",
"## Example results:",
"### Question:",
"### Answer:"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL",
"### Favour:\n\nIt would be highly motivating, if you can STAR⭐ this repo if you find it helpful.",
"## Model\n\nTwo models have been developed for different use cases and they can be found at URL\n\nThe model weights can be found here: GPT-2 and DistilGPT-2",
"### Example usage:",
"## Demo\n![Streamlit App](URL\n\n\nA streamlit webapp has been setup to use the model: URL\n\n!image",
"## Example results:",
"### Question:",
"### Answer:"
] |
[
50,
64,
25,
36,
6,
26,
5,
4,
4
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Leetcode using AI :robot:\nGPT-2 Model for Leetcode Questions in python \n\nNote: the Answers might not make sense in some cases because of the bias in GPT-2\n\nContribtuions: If you would like to make the model better contributions are welcome Check out URL### Favour:\n\nIt would be highly motivating, if you can STAR⭐ this repo if you find it helpful.## Model\n\nTwo models have been developed for different use cases and they can be found at URL\n\nThe model weights can be found here: GPT-2 and DistilGPT-2### Example usage:## Demo\n![Streamlit App](URL\n\n\nA streamlit webapp has been setup to use the model: URL\n\n!image## Example results:### Question:### Answer:"
] |
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] |
null | null |
transformers
|
# Generating Rap song Lyrics like Eminem Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data lyrics from leading hip-hop stars
### My model can be accessed at: gagan3012/rap-writer
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/rap-writer")
model = AutoModelWithLMHead.from_pretrained("gagan3012/rap-writer")
```
|
{}
|
text-generation
|
gagan3012/rap-writer
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Generating Rap song Lyrics like Eminem Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data lyrics from leading hip-hop stars
### My model can be accessed at: gagan3012/rap-writer
|
[
"# Generating Rap song Lyrics like Eminem Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data lyrics from leading hip-hop stars",
"### My model can be accessed at: gagan3012/rap-writer"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Generating Rap song Lyrics like Eminem Using GPT2",
"### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data lyrics from leading hip-hop stars",
"### My model can be accessed at: gagan3012/rap-writer"
] |
[
54,
14,
33,
17
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Generating Rap song Lyrics like Eminem Using GPT2### I have built a custom model for it using data from Kaggle \n\nCreating a new finetuned model using data lyrics from leading hip-hop stars### My model can be accessed at: gagan3012/rap-writer"
] |
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] |
null | null |
transformers
|
---
Summarisation model summarsiation
|
{}
|
text2text-generation
|
gagan3012/summarsiation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
---
Summarisation model summarsiation
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
52
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi", "results": []}]}
|
automatic-speech-recognition
|
gagan3012/wav2vec2-large-xls-r-300m-hindi
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
[
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.11.0"
] |
[
61,
49,
6,
12,
8,
3,
140,
4,
39
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.11.0"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chuvash using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "cv", split="test")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-chuvash")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-chuvash")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
#### Results:
Prediction: ['проектпа килӗшӳллӗн тӗлӗ мероприяти иртермелле', 'твăра çак планета минтӗ пуяни калленнана']
Reference: ['Проектпа килӗшӳллӗн, тӗрлӗ мероприяти ирттермелле.', 'Çак планета питĕ пуян иккен.']
## Evaluation
The model can be evaluated as follows on the Chuvash test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
!mkdir cer
!wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py
test_dataset = load_dataset("common_voice", "cv", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")
cer = load_metric("cer")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-chuvash")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-chuvash")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]' # TODO: adapt this list to include all special characters you removed from the data
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\\twith torch.no_grad():
\\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\\tpred_ids = torch.argmax(logits, dim=-1)
\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\\treturn batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 48.40 %
## Training
The script used for training can be found [here](https://colab.research.google.com/drive/1A7Y20c1QkSHfdOmLXPMiOEpwlTjDZ7m5?usp=sharing)
|
{"language": "cv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-chuvash by Gagan Bhatia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice cv", "type": "common_voice", "args": "cv"}, "metrics": [{"type": "wer", "value": 48.4, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
gagan3012/wav2vec2-xlsr-chuvash
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"cv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"cv"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Results:
Prediction: ['проектпа килӗшӳллӗн тӗлӗ мероприяти иртермелле', 'твăра çак планета минтӗ пуяни калленнана']
Reference: ['Проектпа килӗшӳллӗн, тӗрлӗ мероприяти ирттермелле.', 'Çак планета питĕ пуян иккен.']
## Evaluation
The model can be evaluated as follows on the Chuvash test data of Common Voice.
Test Result: 48.40 %
## Training
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-Chuvash \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Results: \n\nPrediction: ['проектпа килӗшӳллӗн тӗлӗ мероприяти иртермелле', 'твăра çак планета минтӗ пуяни калленнана']\n\nReference: ['Проектпа килӗшӳллӗн, тӗрлӗ мероприяти ирттермелле.', 'Çак планета питĕ пуян иккен.']",
"## Evaluation\n\nThe model can be evaluated as follows on the Chuvash test data of Common Voice.\n\n\n\nTest Result: 48.40 %",
"## Training\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Chuvash \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Results: \n\nPrediction: ['проектпа килӗшӳллӗн тӗлӗ мероприяти иртермелле', 'твăра çак планета минтӗ пуяни калленнана']\n\nReference: ['Проектпа килӗшӳллӗн, тӗрлӗ мероприяти ирттермелле.', 'Çак планета питĕ пуян иккен.']",
"## Evaluation\n\nThe model can be evaluated as follows on the Chuvash test data of Common Voice.\n\n\n\nTest Result: 48.40 %",
"## Training\n\nThe script used for training can be found here"
] |
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20,
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30,
11
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Chuvash \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:#### Results: \n\nPrediction: ['проектпа килӗшӳллӗн тӗлӗ мероприяти иртермелле', 'твăра çак планета минтӗ пуяни калленнана']\n\nReference: ['Проектпа килӗшӳллӗн, тӗрлӗ мероприяти ирттермелле.', 'Çак планета питĕ пуян иккен.']## Evaluation\n\nThe model can be evaluated as follows on the Chuvash test data of Common Voice.\n\n\n\nTest Result: 48.40 %## Training\n\nThe script used for training can be found here"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-khmer
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Khmer using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR Kh](http://www.openslr.org/42/).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
!wget https://www.openslr.org/resources/42/km_kh_male.zip
!unzip km_kh_male.zip
!ls km_kh_male
colnames=['path','sentence']
df = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t',header=None,names = colnames)
df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
train, test = train_test_split(df, test_size=0.1)
test.to_csv('/content/km_kh_male/line_index_test.csv')
test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\\\\\\\\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\\\\\\\\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\\\\\\\\\\\\\\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\\\\\\\\\\\\\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
#### Result
Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from sklearn.model_selection import train_test_split
import pandas as pd
from datasets import load_dataset
!wget https://www.openslr.org/resources/42/km_kh_male.zip
!unzip km_kh_male.zip
!ls km_kh_male
colnames=['path','sentence']
df = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t',header=None,names = colnames)
df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
train, test = train_test_split(df, test_size=0.1)
test.to_csv('/content/km_kh_male/line_index_test.csv')
test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
wer = load_metric("wer")
cer = load_metric("cer")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-khmer")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-khmer")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\tbatch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\\twith torch.no_grad():
\\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\\tpred_ids = torch.argmax(logits, dim=-1)
\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\\treturn batch
cer = load_metric("cer")
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["text"])))
```
**Test Result**: 24.96 %
WER: 24.962519
CER: 6.950925
## Training
The script used for training can be found [here](https://colab.research.google.com/drive/1yo_OTMH8FHQrAKCkKdQGMqpkj-kFhS_2?usp=sharing)
|
{"language": "km", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["OpenSLR", "common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-Khmer by Gagan Bhatia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "OpenSLR km", "type": "OpenSLR", "args": "km"}, "metrics": [{"type": "wer", "value": 24.96, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
gagan3012/wav2vec2-xlsr-khmer
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"km",
"dataset:OpenSLR",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"km"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #km #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-khmer
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Khmer using the Common Voice, and OpenSLR Kh.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Result
Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
Test Result: 24.96 %
WER: 24.962519
CER: 6.950925
## Training
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-khmer \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Khmer using the Common Voice, and OpenSLR Kh. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Result \n\nPrediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']\n\nReference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']",
"## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 24.96 % \n\nWER: 24.962519\nCER: 6.950925",
"## Training\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #km #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-khmer \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Khmer using the Common Voice, and OpenSLR Kh. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Result \n\nPrediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']\n\nReference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']",
"## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 24.96 % \n\nWER: 24.962519\nCER: 6.950925",
"## Training\n\nThe script used for training can be found here"
] |
[
91,
68,
20,
65,
66,
11
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #km #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Wav2Vec2-Large-XLSR-53-khmer \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Khmer using the Common Voice, and OpenSLR Kh. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:#### Result \n\nPrediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']\n\nReference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 24.96 % \n\nWER: 24.962519\nCER: 6.950925## Training\n\nThe script used for training can be found here"
] |
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-0.06654892861843109
] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Nepali
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Nepali using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR ne](http://www.openslr.org/43/).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
!wget https://www.openslr.org/resources/43/ne_np_female.zip
!unzip ne_np_female.zip
!ls ne_np_female
colnames=['path','sentence']
df = pd.read_csv('/content/ne_np_female/line_index.tsv',sep='\\t',header=None,names = colnames)
df['path'] = '/content/ne_np_female/wavs/'+df['path'] +'.wav'
train, test = train_test_split(df, test_size=0.1)
test.to_csv('/content/ne_np_female/line_index_test.csv')
test_dataset = load_dataset('csv', data_files='/content/ne_np_female/line_index_test.csv',split = 'train')
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
#### Result
Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
!wget https://www.openslr.org/resources/43/ne_np_female.zip
!unzip ne_np_female.zip
!ls ne_np_female
colnames=['path','sentence']
df = pd.read_csv('/content/ne_np_female/line_index.tsv',sep='\\t',header=None,names = colnames)
df['path'] = '/content/ne_np_female/wavs/'+df['path'] +'.wav'
train, test = train_test_split(df, test_size=0.1)
test.to_csv('/content/ne_np_female/line_index_test.csv')
test_dataset = load_dataset('csv', data_files='/content/ne_np_female/line_index_test.csv',split = 'train')
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
model.to("cuda")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\tpred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 05.97 %
## Training
The script used for training can be found [here](https://colab.research.google.com/drive/1AHnYWXb5cwfMEa2o4O3TSdasAR3iVBFP?usp=sharing)
|
{"language": "ne", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["OpenSLR", "common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-nepali", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "OpenSLR ne", "type": "OpenSLR", "args": "ne"}, "metrics": [{"type": "wer", "value": 5.97, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
gagan3012/wav2vec2-xlsr-nepali
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ne",
"dataset:OpenSLR",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ne"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ne #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Nepali
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Result
Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
Test Result: 05.97 %
## Training
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-Nepali \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Result \n\nPrediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']\n\nReference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']",
"## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 05.97 %",
"## Training\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ne #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Nepali \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Result \n\nPrediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']\n\nReference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']",
"## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 05.97 %",
"## Training\n\nThe script used for training can be found here"
] |
[
87,
68,
20,
65,
53,
11
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ne #dataset-OpenSLR #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Nepali \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Nepali using the Common Voice, and OpenSLR ne. \n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:#### Result \n\nPrediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']\n\nReference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 05.97 %## Training\n\nThe script used for training can be found here"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "pa-IN", split="test")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-punjabi")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-punjabi")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
#### Results:
Prediction: ['ਹਵਾ ਲਾਤ ਵਿੱਚ ਪੰਦ ਛੇ ਇਖਲਾਟਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈ ਇ ਹਾ ਪੈਸੇ ਲੇਹੜ ਨਹੀਂ ਸੀ ਚੌਨਾ']
Reference: ['ਹਵਾਲਾਤ ਵਿੱਚ ਪੰਜ ਛੇ ਇਖ਼ਲਾਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈਂ ਇਹ ਪੈਸੇ ਲੈਣੇ ਨਹੀਂ ਸੀ ਚਾਹੁੰਦਾ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "pa-IN", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-punjabi")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-punjabi")
model.to("cuda")
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“]' # TODO: adapt this list to include all special characters you removed from the data
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\\\\\\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\\\\\\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\\\\\\\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\\\\\\\\twith torch.no_grad():
\\\\\\\\t\\\\\\\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\\\\\\\\tpred_ids = torch.argmax(logits, dim=-1)
\\\\\\\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\\\\\\\\treturn batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 58.05 %
## Training
The script used for training can be found [here](https://colab.research.google.com/drive/1A7Y20c1QkSHfdOmLXPMiOEpwlTjDZ7m5?usp=sharing)
|
{"language": "pa-IN", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-punjabi", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice pa", "type": "common_voice", "args": "pa-IN"}, "metrics": [{"type": "wer", "value": 58.06, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
gagan3012/wav2vec2-xlsr-punjabi
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pa-IN"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
#### Results:
Prediction: ['ਹਵਾ ਲਾਤ ਵਿੱਚ ਪੰਦ ਛੇ ਇਖਲਾਟਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈ ਇ ਹਾ ਪੈਸੇ ਲੇਹੜ ਨਹੀਂ ਸੀ ਚੌਨਾ']
Reference: ['ਹਵਾਲਾਤ ਵਿੱਚ ਪੰਜ ਛੇ ਇਖ਼ਲਾਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈਂ ਇਹ ਪੈਸੇ ਲੈਣੇ ਨਹੀਂ ਸੀ ਚਾਹੁੰਦਾ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
Test Result: 58.05 %
## Training
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-Punjabi \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Results: \n\nPrediction: ['ਹਵਾ ਲਾਤ ਵਿੱਚ ਪੰਦ ਛੇ ਇਖਲਾਟਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈ ਇ ਹਾ ਪੈਸੇ ਲੇਹੜ ਨਹੀਂ ਸੀ ਚੌਨਾ']\n\nReference: ['ਹਵਾਲਾਤ ਵਿੱਚ ਪੰਜ ਛੇ ਇਖ਼ਲਾਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈਂ ਇਹ ਪੈਸੇ ਲੈਣੇ ਨਹੀਂ ਸੀ ਚਾਹੁੰਦਾ']",
"## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 58.05 %",
"## Training\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Punjabi \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"#### Results: \n\nPrediction: ['ਹਵਾ ਲਾਤ ਵਿੱਚ ਪੰਦ ਛੇ ਇਖਲਾਟਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈ ਇ ਹਾ ਪੈਸੇ ਲੇਹੜ ਨਹੀਂ ਸੀ ਚੌਨਾ']\n\nReference: ['ਹਵਾਲਾਤ ਵਿੱਚ ਪੰਜ ਛੇ ਇਖ਼ਲਾਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈਂ ਇਹ ਪੈਸੇ ਲੈਣੇ ਨਹੀਂ ਸੀ ਚਾਹੁੰਦਾ']",
"## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 58.05 %",
"## Training\n\nThe script used for training can be found here"
] |
[
78,
61,
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54,
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] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Punjabi \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Punjabi using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:#### Results: \n\nPrediction: ['ਹਵਾ ਲਾਤ ਵਿੱਚ ਪੰਦ ਛੇ ਇਖਲਾਟਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈ ਇ ਹਾ ਪੈਸੇ ਲੇਹੜ ਨਹੀਂ ਸੀ ਚੌਨਾ']\n\nReference: ['ਹਵਾਲਾਤ ਵਿੱਚ ਪੰਜ ਛੇ ਇਖ਼ਲਾਕੀ ਮੁਜਰਮ ਸਨ', 'ਮੈਂ ਇਹ ਪੈਸੇ ਲੈਣੇ ਨਹੀਂ ਸੀ ਚਾਹੁੰਦਾ']## Evaluation\n\nThe model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French\n\n\n\n\nTest Result: 58.05 %## Training\n\nThe script used for training can be found here"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-hi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7522
- Wer: 1.0091
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0417 | 2.59 | 500 | 5.1484 | 1.0 |
| 3.3722 | 5.18 | 1000 | 3.3380 | 1.0001 |
| 1.9752 | 7.77 | 1500 | 1.3910 | 1.0074 |
| 1.5868 | 10.36 | 2000 | 1.0298 | 1.0084 |
| 1.4413 | 12.95 | 2500 | 0.9313 | 1.0175 |
| 1.3296 | 15.54 | 3000 | 0.8966 | 1.0194 |
| 1.2746 | 18.13 | 3500 | 0.8875 | 1.0097 |
| 1.2147 | 20.73 | 4000 | 0.8746 | 1.0089 |
| 1.1774 | 23.32 | 4500 | 0.8383 | 1.0198 |
| 1.129 | 25.91 | 5000 | 0.7848 | 1.0167 |
| 1.0995 | 28.5 | 5500 | 0.7992 | 1.0210 |
| 1.0665 | 31.09 | 6000 | 0.7878 | 1.0107 |
| 1.0321 | 33.68 | 6500 | 0.7653 | 1.0082 |
| 1.0068 | 36.27 | 7000 | 0.7635 | 1.0065 |
| 0.9916 | 38.86 | 7500 | 0.7728 | 1.0090 |
| 0.9735 | 41.45 | 8000 | 0.7688 | 1.0070 |
| 0.9745 | 44.04 | 8500 | 0.7455 | 1.0097 |
| 0.9677 | 46.63 | 9000 | 0.7605 | 1.0099 |
| 0.9313 | 49.22 | 9500 | 0.7527 | 1.0097 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
{"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "xls-r-300m-hi", "results": []}]}
|
automatic-speech-recognition
|
gagan3012/xls-r-300m-hi
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
xls-r-300m-hi
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7522
* Wer: 1.0091
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
[
79,
160,
4,
39
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-pa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0443
- Wer: 0.5715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 500.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 4.6694 | 19.22 | 500 | 4.0455 | 1.0 |
| 3.3907 | 38.45 | 1000 | 3.2836 | 1.0 |
| 2.0866 | 57.67 | 1500 | 1.2788 | 0.7715 |
| 1.4106 | 76.9 | 2000 | 0.7866 | 0.6891 |
| 1.1711 | 96.15 | 2500 | 0.6556 | 0.6272 |
| 1.038 | 115.37 | 3000 | 0.6195 | 0.5680 |
| 0.8989 | 134.6 | 3500 | 0.6563 | 0.5602 |
| 0.8021 | 153.82 | 4000 | 0.6644 | 0.5327 |
| 0.7161 | 173.07 | 4500 | 0.6844 | 0.5253 |
| 0.6449 | 192.3 | 5000 | 0.7018 | 0.5331 |
| 0.5659 | 211.52 | 5500 | 0.7451 | 0.5465 |
| 0.5118 | 230.75 | 6000 | 0.7857 | 0.5386 |
| 0.4385 | 249.97 | 6500 | 0.8062 | 0.5382 |
| 0.3984 | 269.22 | 7000 | 0.8316 | 0.5621 |
| 0.3666 | 288.45 | 7500 | 0.8736 | 0.5504 |
| 0.3256 | 307.67 | 8000 | 0.9133 | 0.5688 |
| 0.289 | 326.9 | 8500 | 0.9556 | 0.5684 |
| 0.2663 | 346.15 | 9000 | 0.9344 | 0.5708 |
| 0.2445 | 365.37 | 9500 | 0.9472 | 0.5590 |
| 0.2289 | 384.6 | 10000 | 0.9713 | 0.5672 |
| 0.2048 | 403.82 | 10500 | 0.9978 | 0.5762 |
| 0.1857 | 423.07 | 11000 | 1.0230 | 0.5798 |
| 0.1751 | 442.3 | 11500 | 1.0409 | 0.5755 |
| 0.1688 | 461.52 | 12000 | 1.0445 | 0.5727 |
| 0.1633 | 480.75 | 12500 | 1.0484 | 0.5739 |
| 0.1488 | 499.97 | 13000 | 1.0443 | 0.5715 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
{"language": ["pa-IN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "xls-r-300m-pa", "results": []}]}
|
automatic-speech-recognition
|
gagan3012/xls-r-300m-pa
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pa-IN"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
xls-r-300m-pa
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PA-IN dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0443
* Wer: 0.5715
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 500.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 500.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 500.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
[
77,
160,
4,
39
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 500.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 19984005
- CO2 Emissions (in grams): 20.790169878009916
## Validation Metrics
- Loss: 0.06693269312381744
- Accuracy: 0.9789
- Precision: 0.9843244336569579
- Recall: 0.9733
- AUC: 0.99695552
- F1: 0.9787811745776348
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/gagandeepkundi/autonlp-text-classification-19984005
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("gagandeepkundi/autonlp-text-classification-19984005", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("gagandeepkundi/autonlp-text-classification-19984005", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "es", "tags": "autonlp", "datasets": ["gagandeepkundi/autonlp-data-text-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 20.790169878009916}
|
text-classification
|
gagandeepkundi/latam-question-quality
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"es",
"dataset:gagandeepkundi/autonlp-data-text-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #es #dataset-gagandeepkundi/autonlp-data-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 19984005
- CO2 Emissions (in grams): 20.790169878009916
## Validation Metrics
- Loss: 0.06693269312381744
- Accuracy: 0.9789
- Precision: 0.9843244336569579
- Recall: 0.9733
- AUC: 0.99695552
- F1: 0.9787811745776348
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 19984005\n- CO2 Emissions (in grams): 20.790169878009916",
"## Validation Metrics\n\n- Loss: 0.06693269312381744\n- Accuracy: 0.9789\n- Precision: 0.9843244336569579\n- Recall: 0.9733\n- AUC: 0.99695552\n- F1: 0.9787811745776348",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #es #dataset-gagandeepkundi/autonlp-data-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 19984005\n- CO2 Emissions (in grams): 20.790169878009916",
"## Validation Metrics\n\n- Loss: 0.06693269312381744\n- Accuracy: 0.9789\n- Precision: 0.9843244336569579\n- Recall: 0.9733\n- AUC: 0.99695552\n- F1: 0.9787811745776348",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
71,
41,
67,
17
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #es #dataset-gagandeepkundi/autonlp-data-text-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 19984005\n- CO2 Emissions (in grams): 20.790169878009916## Validation Metrics\n\n- Loss: 0.06693269312381744\n- Accuracy: 0.9789\n- Precision: 0.9843244336569579\n- Recall: 0.9733\n- AUC: 0.99695552\n- F1: 0.9787811745776348## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Sentiment Classification for hinglish text: `gk-hinglish-sentiment`
## Model description
Trained small amount of reviews dataset
## Intended uses & limitations
I wanted something to work well with hinglish data as it is being used in India mostly.
The training data was not much as expected
#### How to use
```python
#sample code
from transformers import BertTokenizer, BertForSequenceClassification
tokenizerg = BertTokenizer.from_pretrained("/content/model")
modelg = BertForSequenceClassification.from_pretrained("/content/model")
text = "kuch bhi type karo hinglish mai"
encoded_input = tokenizerg(text, return_tensors='pt')
output = modelg(**encoded_input)
print(output)
#output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive
```
#### Limitations and bias
The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data
## Training data
Training data contains labeled data for 3 labels
link to the pre-trained model card with description of the pre-training data.
I have Tuned below model
https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment
### BibTeX entry and citation info
```@inproceedings{khanuja-etal-2020-gluecos,
title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
author = "Khanuja, Simran and
Dandapat, Sandipan and
Srinivasan, Anirudh and
Sitaram, Sunayana and
Choudhury, Monojit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.329",
pages = "3575--3585"
}
```
|
{"license": "apache-2.0", "tags": ["sentiment", "multilingual", "hindi codemix", "hinglish"], "datasets": ["sail"], "language_bcp47": ["hi-en"]}
|
text-classification
|
ganeshkharad/gk-hinglish-sentiment
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"multilingual",
"hindi codemix",
"hinglish",
"dataset:sail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #sentiment #multilingual #hindi codemix #hinglish #dataset-sail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'
## Model description
Trained small amount of reviews dataset
## Intended uses & limitations
I wanted something to work well with hinglish data as it is being used in India mostly.
The training data was not much as expected
#### How to use
#### Limitations and bias
The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data
## Training data
Training data contains labeled data for 3 labels
link to the pre-trained model card with description of the pre-training data.
I have Tuned below model
URL
### BibTeX entry and citation info
|
[
"# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'",
"## Model description\n\nTrained small amount of reviews dataset",
"## Intended uses & limitations\n\nI wanted something to work well with hinglish data as it is being used in India mostly.\nThe training data was not much as expected",
"#### How to use",
"#### Limitations and bias\n\nThe data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data",
"## Training data\n\nTraining data contains labeled data for 3 labels\n\nlink to the pre-trained model card with description of the pre-training data.\nI have Tuned below model\n\nURL",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #sentiment #multilingual #hindi codemix #hinglish #dataset-sail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'",
"## Model description\n\nTrained small amount of reviews dataset",
"## Intended uses & limitations\n\nI wanted something to work well with hinglish data as it is being used in India mostly.\nThe training data was not much as expected",
"#### How to use",
"#### Limitations and bias\n\nThe data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data",
"## Training data\n\nTraining data contains labeled data for 3 labels\n\nlink to the pre-trained model card with description of the pre-training data.\nI have Tuned below model\n\nURL",
"### BibTeX entry and citation info"
] |
[
76,
20,
11,
36,
5,
39,
39,
11
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #sentiment #multilingual #hindi codemix #hinglish #dataset-sail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Sentiment Classification for hinglish text: 'gk-hinglish-sentiment'## Model description\n\nTrained small amount of reviews dataset## Intended uses & limitations\n\nI wanted something to work well with hinglish data as it is being used in India mostly.\nThe training data was not much as expected#### How to use#### Limitations and bias\n\nThe data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data## Training data\n\nTraining data contains labeled data for 3 labels\n\nlink to the pre-trained model card with description of the pre-training data.\nI have Tuned below model\n\nURL### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
## Generating Chinese poetry by topic.
```python
from transformers import *
tokenizer = BertTokenizer.from_pretrained("gaochangkuan/model_dir")
model = AutoModelWithLMHead.from_pretrained("gaochangkuan/model_dir")
prompt= '''<s>田园躬耕'''
length= 84
stop_token='</s>'
temperature = 1.2
repetition_penalty=1.3
k= 30
p= 0.95
device ='cuda'
seed=2020
no_cuda=False
prompt_text = prompt if prompt else input("Model prompt >>> ")
encoded_prompt = tokenizer.encode(
'<s>'+prompt_text+'<sep>',
add_special_tokens=False,
return_tensors="pt"
)
encoded_prompt = encoded_prompt.to(device)
output_sequences = model.generate(
input_ids=encoded_prompt,
max_length=length,
min_length=10,
do_sample=True,
early_stopping=True,
num_beams=10,
temperature=temperature,
top_k=k,
top_p=p,
repetition_penalty=repetition_penalty,
bad_words_ids=None,
bos_token_id=tokenizer.bos_token_id,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
length_penalty=1.2,
no_repeat_ngram_size=2,
num_return_sequences=1,
attention_mask=None,
decoder_start_token_id=tokenizer.bos_token_id,)
generated_sequence = output_sequences[0].tolist()
text = tokenizer.decode(generated_sequence)
text = text[: text.find(stop_token) if stop_token else None]
print(''.join(text).replace(' ','').replace('<pad>','').replace('<s>',''))
```
|
{}
|
text-generation
|
gaochangkuan/model_dir
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Generating Chinese poetry by topic.
|
[
"## Generating Chinese poetry by topic."
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Generating Chinese poetry by topic."
] |
[
50,
9
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Generating Chinese poetry by topic."
] |
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] |
null | null |
transformers
|
### What style is that?
This model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training.
Upload a photograph of a building to the File Uploader on the right. The Image Classifier will predict its architectural style using a database of over 700 images. Scroll down to read more about each style.
### Classical Revival (1895 - 1950)
The Classical Revival or Neoclassical style is one of the most commonly seen across the state and the country. This style was inspired by the World's Columbian Exposition in Chicago held in 1893 which promoted a renewed interest in the classical forms. This style encompasses many different styles, including Colonial Revival, Greek Revival, Neoclassical Revival and Mediterranean Revival. Colonial Revival is most commonly used in residential dwellings, while Greek and Neoclassical Revival styles are commonly used in commercial buildings like banks, post offices, and municipal buildings.
#### Queen Anne (1880-1910)
The Queen Anne style was one of a number of popular architectural styles that emerged in the United States during the Victorian Period. It ranges from high style, like the image pictured here, to more vernacular styles that exhibit the Queen Anne form without its high style architectural details.
#### Craftsman Bungalow (1900-1930)
The terms “craftsman” and “bungalow” are often used interchangably, however, “craftsman” refers to the Arts and Crafts movement and is considered an architectural style, whereas “bungalow” is the form of house. Bungalows often exhibit a craftsman style.
#### Tudor Cottage (1910-1950)
Tudor homes are inspired by the Medieval period and can range is size and style. In general, the Tudor style features steeply pitched roofs, often with a cat-slide roof line, predominately brick construction, sometimes accented with half-timber framing, front-facing, prominently placed brick or stone chimneys, and tall windows with rectangular or diamond-shaped panes. Front doors are typically off-center with a round arch at the top of the door or doorway.
#### Mid-Century Modern Ranch (1930-1970)
The Ranch style originated in southern California in the mid-1930s. In the 1940s, the Ranch was one of the small house types financed by the Federal Housing Administration (FHA), along with Minimal Traditional and other small house styles. The Ranch house began to pick up popularity as the financial controls that encouraged small house building lifted following WWII; by the 1950s it was the most predominant residential style in the country.
This model was created with HuggingPics🤗🖼️ Image Classifier!
Make your own!: [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
gatecitypreservation/architectural_styles
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### What style is that?
This model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training.
Upload a photograph of a building to the File Uploader on the right. The Image Classifier will predict its architectural style using a database of over 700 images. Scroll down to read more about each style.
### Classical Revival (1895 - 1950)
The Classical Revival or Neoclassical style is one of the most commonly seen across the state and the country. This style was inspired by the World's Columbian Exposition in Chicago held in 1893 which promoted a renewed interest in the classical forms. This style encompasses many different styles, including Colonial Revival, Greek Revival, Neoclassical Revival and Mediterranean Revival. Colonial Revival is most commonly used in residential dwellings, while Greek and Neoclassical Revival styles are commonly used in commercial buildings like banks, post offices, and municipal buildings.
!classical revival architecture
#### Queen Anne (1880-1910)
The Queen Anne style was one of a number of popular architectural styles that emerged in the United States during the Victorian Period. It ranges from high style, like the image pictured here, to more vernacular styles that exhibit the Queen Anne form without its high style architectural details.
!queen anne architecture
#### Craftsman Bungalow (1900-1930)
The terms “craftsman” and “bungalow” are often used interchangably, however, “craftsman” refers to the Arts and Crafts movement and is considered an architectural style, whereas “bungalow” is the form of house. Bungalows often exhibit a craftsman style.
!craftsman bungalow architecture
#### Tudor Cottage (1910-1950)
Tudor homes are inspired by the Medieval period and can range is size and style. In general, the Tudor style features steeply pitched roofs, often with a cat-slide roof line, predominately brick construction, sometimes accented with half-timber framing, front-facing, prominently placed brick or stone chimneys, and tall windows with rectangular or diamond-shaped panes. Front doors are typically off-center with a round arch at the top of the door or doorway.
!tudor cottage architecture
#### Mid-Century Modern Ranch (1930-1970)
The Ranch style originated in southern California in the mid-1930s. In the 1940s, the Ranch was one of the small house types financed by the Federal Housing Administration (FHA), along with Minimal Traditional and other small house styles. The Ranch house began to pick up popularity as the financial controls that encouraged small house building lifted following WWII; by the 1950s it was the most predominant residential style in the country.
!mid-century modern ranch
This model was created with HuggingPics️ Image Classifier!
Make your own!: the demo on Google Colab.
|
[
"### What style is that?\n\nThis model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training. \n\nUpload a photograph of a building to the File Uploader on the right. The Image Classifier will predict its architectural style using a database of over 700 images. Scroll down to read more about each style.",
"### Classical Revival (1895 - 1950)\n\nThe Classical Revival or Neoclassical style is one of the most commonly seen across the state and the country. This style was inspired by the World's Columbian Exposition in Chicago held in 1893 which promoted a renewed interest in the classical forms. This style encompasses many different styles, including Colonial Revival, Greek Revival, Neoclassical Revival and Mediterranean Revival. Colonial Revival is most commonly used in residential dwellings, while Greek and Neoclassical Revival styles are commonly used in commercial buildings like banks, post offices, and municipal buildings. \n\n!classical revival architecture",
"#### Queen Anne (1880-1910)\n\nThe Queen Anne style was one of a number of popular architectural styles that emerged in the United States during the Victorian Period. It ranges from high style, like the image pictured here, to more vernacular styles that exhibit the Queen Anne form without its high style architectural details.\n\n!queen anne architecture",
"#### Craftsman Bungalow (1900-1930)\n\nThe terms “craftsman” and “bungalow” are often used interchangably, however, “craftsman” refers to the Arts and Crafts movement and is considered an architectural style, whereas “bungalow” is the form of house. Bungalows often exhibit a craftsman style.\n\n!craftsman bungalow architecture",
"#### Tudor Cottage (1910-1950)\n\nTudor homes are inspired by the Medieval period and can range is size and style. In general, the Tudor style features steeply pitched roofs, often with a cat-slide roof line, predominately brick construction, sometimes accented with half-timber framing, front-facing, prominently placed brick or stone chimneys, and tall windows with rectangular or diamond-shaped panes. Front doors are typically off-center with a round arch at the top of the door or doorway. \n\n!tudor cottage architecture",
"#### Mid-Century Modern Ranch (1930-1970)\n\nThe Ranch style originated in southern California in the mid-1930s. In the 1940s, the Ranch was one of the small house types financed by the Federal Housing Administration (FHA), along with Minimal Traditional and other small house styles. The Ranch house began to pick up popularity as the financial controls that encouraged small house building lifted following WWII; by the 1950s it was the most predominant residential style in the country.\n\n!mid-century modern ranch\n\nThis model was created with HuggingPics️ Image Classifier! \nMake your own!: the demo on Google Colab."
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### What style is that?\n\nThis model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training. \n\nUpload a photograph of a building to the File Uploader on the right. The Image Classifier will predict its architectural style using a database of over 700 images. Scroll down to read more about each style.",
"### Classical Revival (1895 - 1950)\n\nThe Classical Revival or Neoclassical style is one of the most commonly seen across the state and the country. This style was inspired by the World's Columbian Exposition in Chicago held in 1893 which promoted a renewed interest in the classical forms. This style encompasses many different styles, including Colonial Revival, Greek Revival, Neoclassical Revival and Mediterranean Revival. Colonial Revival is most commonly used in residential dwellings, while Greek and Neoclassical Revival styles are commonly used in commercial buildings like banks, post offices, and municipal buildings. \n\n!classical revival architecture",
"#### Queen Anne (1880-1910)\n\nThe Queen Anne style was one of a number of popular architectural styles that emerged in the United States during the Victorian Period. It ranges from high style, like the image pictured here, to more vernacular styles that exhibit the Queen Anne form without its high style architectural details.\n\n!queen anne architecture",
"#### Craftsman Bungalow (1900-1930)\n\nThe terms “craftsman” and “bungalow” are often used interchangably, however, “craftsman” refers to the Arts and Crafts movement and is considered an architectural style, whereas “bungalow” is the form of house. Bungalows often exhibit a craftsman style.\n\n!craftsman bungalow architecture",
"#### Tudor Cottage (1910-1950)\n\nTudor homes are inspired by the Medieval period and can range is size and style. In general, the Tudor style features steeply pitched roofs, often with a cat-slide roof line, predominately brick construction, sometimes accented with half-timber framing, front-facing, prominently placed brick or stone chimneys, and tall windows with rectangular or diamond-shaped panes. Front doors are typically off-center with a round arch at the top of the door or doorway. \n\n!tudor cottage architecture",
"#### Mid-Century Modern Ranch (1930-1970)\n\nThe Ranch style originated in southern California in the mid-1930s. In the 1940s, the Ranch was one of the small house types financed by the Federal Housing Administration (FHA), along with Minimal Traditional and other small house styles. The Ranch house began to pick up popularity as the financial controls that encouraged small house building lifted following WWII; by the 1950s it was the most predominant residential style in the country.\n\n!mid-century modern ranch\n\nThis model was created with HuggingPics️ Image Classifier! \nMake your own!: the demo on Google Colab."
] |
[
53,
98,
154,
78,
85,
133,
149
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### What style is that?\n\nThis model can help identify five architectural styles that were prominent in the early to mid 20th century. Check back for updates including more architectural styles and more accurate predictions as this model diversifies and improves its training. \n\nUpload a photograph of a building to the File Uploader on the right. The Image Classifier will predict its architectural style using a database of over 700 images. Scroll down to read more about each style.### Classical Revival (1895 - 1950)\n\nThe Classical Revival or Neoclassical style is one of the most commonly seen across the state and the country. This style was inspired by the World's Columbian Exposition in Chicago held in 1893 which promoted a renewed interest in the classical forms. This style encompasses many different styles, including Colonial Revival, Greek Revival, Neoclassical Revival and Mediterranean Revival. Colonial Revival is most commonly used in residential dwellings, while Greek and Neoclassical Revival styles are commonly used in commercial buildings like banks, post offices, and municipal buildings. \n\n!classical revival architecture#### Queen Anne (1880-1910)\n\nThe Queen Anne style was one of a number of popular architectural styles that emerged in the United States during the Victorian Period. It ranges from high style, like the image pictured here, to more vernacular styles that exhibit the Queen Anne form without its high style architectural details.\n\n!queen anne architecture#### Craftsman Bungalow (1900-1930)\n\nThe terms “craftsman” and “bungalow” are often used interchangably, however, “craftsman” refers to the Arts and Crafts movement and is considered an architectural style, whereas “bungalow” is the form of house. Bungalows often exhibit a craftsman style.\n\n!craftsman bungalow architecture"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7550
- Matthews Correlation: 0.5265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5296 | 1.0 | 535 | 0.5144 | 0.4215 |
| 0.3504 | 2.0 | 1070 | 0.4903 | 0.5046 |
| 0.2393 | 3.0 | 1605 | 0.6339 | 0.5058 |
| 0.175 | 4.0 | 2140 | 0.7550 | 0.5265 |
| 0.1259 | 5.0 | 2675 | 0.8688 | 0.5259 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5264763891845121, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
gauravtripathy/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7550
* Matthews Correlation: 0.5265
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
sentence-transformers
|
# gaussfer/test_simcse_new
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('gaussfer/test_simcse_new')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('gaussfer/test_simcse_new')
model = AutoModel.from_pretrained('gaussfer/test_simcse_new')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=gaussfer/test_simcse_new)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 40,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
gaussfer/test_simcse_new
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gaussfer/test_simcse_new
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 875 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
|
[
"# gaussfer/test_simcse_new\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 875 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gaussfer/test_simcse_new\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 875 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
[
42,
54,
38,
64,
29,
86,
5,
6
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# gaussfer/test_simcse_new\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 875 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5363
- Rouge2 Precision: 0.3459
- Rouge2 Recall: 0.2455
- Rouge2 Fmeasure: 0.2731
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.652 | 1.0 | 1125 | 1.5087 | 0.3647 | 0.2425 | 0.2772 |
| 1.4695 | 2.0 | 2250 | 1.5039 | 0.3448 | 0.2457 | 0.2732 |
| 1.3714 | 3.0 | 3375 | 1.4842 | 0.3509 | 0.2474 | 0.277 |
| 1.2734 | 4.0 | 4500 | 1.4901 | 0.3452 | 0.2426 | 0.2716 |
| 1.1853 | 5.0 | 5625 | 1.5152 | 0.3658 | 0.2371 | 0.2744 |
| 1.0975 | 6.0 | 6750 | 1.5133 | 0.3529 | 0.2417 | 0.2729 |
| 1.0448 | 7.0 | 7875 | 1.5203 | 0.3485 | 0.2464 | 0.275 |
| 0.9999 | 8.0 | 9000 | 1.5316 | 0.3437 | 0.2435 | 0.2719 |
| 0.9732 | 9.0 | 10125 | 1.5338 | 0.3464 | 0.2446 | 0.2732 |
| 0.954 | 10.0 | 11250 | 1.5363 | 0.3459 | 0.2455 | 0.2731 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-finetuned-pubmed", "results": []}]}
|
text2text-generation
|
gayanin/bart-finetuned-pubmed
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-finetuned-pubmed
=====================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5363
* Rouge2 Precision: 0.3459
* Rouge2 Recall: 0.2455
* Rouge2 Fmeasure: 0.2731
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-15
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4822
- Rouge2 Precision: 0.7578
- Rouge2 Recall: 0.5933
- Rouge2 Fmeasure: 0.6511
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.7006 | 1.0 | 663 | 0.5062 | 0.7492 | 0.5855 | 0.6434 |
| 0.5709 | 2.0 | 1326 | 0.4811 | 0.7487 | 0.5879 | 0.6447 |
| 0.5011 | 3.0 | 1989 | 0.4734 | 0.7541 | 0.5906 | 0.6483 |
| 0.4164 | 4.0 | 2652 | 0.4705 | 0.7515 | 0.5876 | 0.6452 |
| 0.3888 | 5.0 | 3315 | 0.4703 | 0.7555 | 0.5946 | 0.6515 |
| 0.3655 | 6.0 | 3978 | 0.4725 | 0.7572 | 0.5943 | 0.6516 |
| 0.319 | 7.0 | 4641 | 0.4733 | 0.7557 | 0.5911 | 0.6491 |
| 0.3089 | 8.0 | 5304 | 0.4792 | 0.7577 | 0.5936 | 0.6513 |
| 0.2907 | 9.0 | 5967 | 0.4799 | 0.7577 | 0.5931 | 0.6509 |
| 0.275 | 10.0 | 6630 | 0.4822 | 0.7578 | 0.5933 | 0.6511 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-15", "results": []}]}
|
text2text-generation
|
gayanin/bart-mlm-pubmed-15
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-mlm-pubmed-15
==================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4822
* Rouge2 Precision: 0.7578
* Rouge2 Recall: 0.5933
* Rouge2 Fmeasure: 0.6511
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-35
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9359
- Rouge2 Precision: 0.5451
- Rouge2 Recall: 0.4232
- Rouge2 Fmeasure: 0.4666
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.4156 | 1.0 | 663 | 1.0366 | 0.5165 | 0.3967 | 0.4394 |
| 1.1773 | 2.0 | 1326 | 0.9841 | 0.5354 | 0.4168 | 0.4589 |
| 1.0894 | 3.0 | 1989 | 0.9554 | 0.5346 | 0.4133 | 0.4563 |
| 0.9359 | 4.0 | 2652 | 0.9440 | 0.5357 | 0.4163 | 0.4587 |
| 0.8758 | 5.0 | 3315 | 0.9340 | 0.5428 | 0.4226 | 0.465 |
| 0.8549 | 6.0 | 3978 | 0.9337 | 0.5385 | 0.422 | 0.4634 |
| 0.7743 | 7.0 | 4641 | 0.9330 | 0.542 | 0.422 | 0.4647 |
| 0.7465 | 8.0 | 5304 | 0.9315 | 0.5428 | 0.4231 | 0.4654 |
| 0.7348 | 9.0 | 5967 | 0.9344 | 0.5462 | 0.4244 | 0.4674 |
| 0.7062 | 10.0 | 6630 | 0.9359 | 0.5451 | 0.4232 | 0.4666 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-35", "results": []}]}
|
text2text-generation
|
gayanin/bart-mlm-pubmed-35
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-mlm-pubmed-35
==================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9359
* Rouge2 Precision: 0.5451
* Rouge2 Recall: 0.4232
* Rouge2 Fmeasure: 0.4666
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-45
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1797
- Rouge2 Precision: 0.4333
- Rouge2 Recall: 0.3331
- Rouge2 Fmeasure: 0.3684
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.7989 | 1.0 | 663 | 1.3385 | 0.4097 | 0.3086 | 0.3444 |
| 1.5072 | 2.0 | 1326 | 1.2582 | 0.4218 | 0.3213 | 0.3569 |
| 1.4023 | 3.0 | 1989 | 1.2236 | 0.4207 | 0.3211 | 0.3562 |
| 1.2205 | 4.0 | 2652 | 1.2025 | 0.4359 | 0.3331 | 0.3696 |
| 1.1584 | 5.0 | 3315 | 1.1910 | 0.4304 | 0.3307 | 0.3658 |
| 1.1239 | 6.0 | 3978 | 1.1830 | 0.4247 | 0.3279 | 0.3618 |
| 1.0384 | 7.0 | 4641 | 1.1761 | 0.4308 | 0.3325 | 0.367 |
| 1.0168 | 8.0 | 5304 | 1.1762 | 0.4314 | 0.3336 | 0.368 |
| 0.9966 | 9.0 | 5967 | 1.1773 | 0.4335 | 0.3341 | 0.369 |
| 0.961 | 10.0 | 6630 | 1.1797 | 0.4333 | 0.3331 | 0.3684 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-45", "results": []}]}
|
text2text-generation
|
gayanin/bart-mlm-pubmed-45
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-mlm-pubmed-45
==================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1797
* Rouge2 Precision: 0.4333
* Rouge2 Recall: 0.3331
* Rouge2 Fmeasure: 0.3684
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed-medterm
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge2 Precision: 0.985
- Rouge2 Recall: 0.7208
- Rouge2 Fmeasure: 0.8088
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.0018 | 1.0 | 13833 | 0.0003 | 0.985 | 0.7208 | 0.8088 |
| 0.0014 | 2.0 | 27666 | 0.0006 | 0.9848 | 0.7207 | 0.8086 |
| 0.0009 | 3.0 | 41499 | 0.0002 | 0.9848 | 0.7207 | 0.8086 |
| 0.0007 | 4.0 | 55332 | 0.0002 | 0.985 | 0.7208 | 0.8088 |
| 0.0006 | 5.0 | 69165 | 0.0001 | 0.9848 | 0.7207 | 0.8087 |
| 0.0001 | 6.0 | 82998 | 0.0002 | 0.9846 | 0.7206 | 0.8086 |
| 0.0009 | 7.0 | 96831 | 0.0001 | 0.9848 | 0.7208 | 0.8087 |
| 0.0 | 8.0 | 110664 | 0.0000 | 0.9848 | 0.7207 | 0.8087 |
| 0.0001 | 9.0 | 124497 | 0.0000 | 0.985 | 0.7208 | 0.8088 |
| 0.0 | 10.0 | 138330 | 0.0000 | 0.985 | 0.7208 | 0.8088 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed-medterm", "results": []}]}
|
text2text-generation
|
gayanin/bart-mlm-pubmed-medterm
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-mlm-pubmed-medterm
=======================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Rouge2 Precision: 0.985
* Rouge2 Recall: 0.7208
* Rouge2 Fmeasure: 0.8088
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-mlm-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7223
- Rouge2 Precision: 0.6572
- Rouge2 Recall: 0.5164
- Rouge2 Fmeasure: 0.5662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.0322 | 1.0 | 663 | 0.7891 | 0.639 | 0.4989 | 0.5491 |
| 0.8545 | 2.0 | 1326 | 0.7433 | 0.6461 | 0.5057 | 0.5556 |
| 0.758 | 3.0 | 1989 | 0.7299 | 0.647 | 0.5033 | 0.5547 |
| 0.6431 | 4.0 | 2652 | 0.7185 | 0.6556 | 0.5101 | 0.5616 |
| 0.6058 | 5.0 | 3315 | 0.7126 | 0.6537 | 0.5144 | 0.5638 |
| 0.5726 | 6.0 | 3978 | 0.7117 | 0.6567 | 0.5169 | 0.5666 |
| 0.5168 | 7.0 | 4641 | 0.7150 | 0.6585 | 0.5154 | 0.566 |
| 0.5011 | 8.0 | 5304 | 0.7220 | 0.6568 | 0.5164 | 0.5664 |
| 0.4803 | 9.0 | 5967 | 0.7208 | 0.6573 | 0.5161 | 0.5662 |
| 0.4577 | 10.0 | 6630 | 0.7223 | 0.6572 | 0.5164 | 0.5662 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-mlm-pubmed", "results": []}]}
|
text2text-generation
|
gayanin/bart-mlm-pubmed
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-mlm-pubmed
===============
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7223
* Rouge2 Precision: 0.6572
* Rouge2 Recall: 0.5164
* Rouge2 Fmeasure: 0.5662
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-pubmed-1.1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4236
- Rouge2 Precision: 0.8482
- Rouge2 Recall: 0.673
- Rouge2 Fmeasure: 0.7347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6534 | 1.0 | 663 | 0.4641 | 0.8448 | 0.6691 | 0.7313 |
| 0.5078 | 2.0 | 1326 | 0.4398 | 0.8457 | 0.6719 | 0.7333 |
| 0.4367 | 3.0 | 1989 | 0.4274 | 0.847 | 0.6717 | 0.7335 |
| 0.3575 | 4.0 | 2652 | 0.4149 | 0.8481 | 0.6733 | 0.735 |
| 0.3319 | 5.0 | 3315 | 0.4170 | 0.8481 | 0.6724 | 0.7343 |
| 0.3179 | 6.0 | 3978 | 0.4264 | 0.8484 | 0.6733 | 0.735 |
| 0.2702 | 7.0 | 4641 | 0.4207 | 0.8489 | 0.6732 | 0.7353 |
| 0.2606 | 8.0 | 5304 | 0.4205 | 0.8487 | 0.6725 | 0.7347 |
| 0.2496 | 9.0 | 5967 | 0.4247 | 0.8466 | 0.6717 | 0.7334 |
| 0.2353 | 10.0 | 6630 | 0.4236 | 0.8482 | 0.673 | 0.7347 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrase-pubmed-1.1", "results": []}]}
|
text2text-generation
|
gayanin/bart-paraphrase-pubmed-1.1
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-paraphrase-pubmed-1.1
==========================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4236
* Rouge2 Precision: 0.8482
* Rouge2 Recall: 0.673
* Rouge2 Fmeasure: 0.7347
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6340
- Rouge2 Precision: 0.83
- Rouge2 Recall: 0.6526
- Rouge2 Fmeasure: 0.7144
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6613 | 1.0 | 663 | 0.4750 | 0.8321 | 0.6552 | 0.7167 |
| 0.4993 | 2.0 | 1326 | 0.4404 | 0.8366 | 0.6583 | 0.7203 |
| 0.443 | 3.0 | 1989 | 0.4261 | 0.8319 | 0.6562 | 0.7176 |
| 0.3482 | 4.0 | 2652 | 0.4198 | 0.8348 | 0.6571 | 0.7191 |
| 0.3206 | 5.0 | 3315 | 0.4233 | 0.8344 | 0.656 | 0.7183 |
| 0.294 | 6.0 | 3978 | 0.4334 | 0.835 | 0.657 | 0.719 |
| 0.2404 | 7.0 | 4641 | 0.4437 | 0.8334 | 0.6559 | 0.7178 |
| 0.2228 | 8.0 | 5304 | 0.4438 | 0.8348 | 0.6565 | 0.7187 |
| 0.211 | 9.0 | 5967 | 0.4516 | 0.8329 | 0.6549 | 0.717 |
| 0.1713 | 10.0 | 6630 | 0.4535 | 0.8332 | 0.6547 | 0.7169 |
| 0.1591 | 11.0 | 7293 | 0.4763 | 0.8349 | 0.6561 | 0.7184 |
| 0.1555 | 12.0 | 7956 | 0.4824 | 0.8311 | 0.6534 | 0.7153 |
| 0.1262 | 13.0 | 8619 | 0.4883 | 0.8322 | 0.655 | 0.7167 |
| 0.1164 | 14.0 | 9282 | 0.5025 | 0.8312 | 0.6539 | 0.7158 |
| 0.1108 | 15.0 | 9945 | 0.5149 | 0.8321 | 0.6535 | 0.7157 |
| 0.0926 | 16.0 | 10608 | 0.5340 | 0.8315 | 0.6544 | 0.7159 |
| 0.0856 | 17.0 | 11271 | 0.5322 | 0.8306 | 0.6518 | 0.7142 |
| 0.0785 | 18.0 | 11934 | 0.5346 | 0.8324 | 0.6549 | 0.7167 |
| 0.071 | 19.0 | 12597 | 0.5488 | 0.8311 | 0.652 | 0.714 |
| 0.0635 | 20.0 | 13260 | 0.5624 | 0.8287 | 0.6517 | 0.7132 |
| 0.0608 | 21.0 | 13923 | 0.5612 | 0.8299 | 0.6527 | 0.7141 |
| 0.0531 | 22.0 | 14586 | 0.5764 | 0.8283 | 0.6498 | 0.7119 |
| 0.0486 | 23.0 | 15249 | 0.5832 | 0.8298 | 0.6532 | 0.7148 |
| 0.0465 | 24.0 | 15912 | 0.5866 | 0.83 | 0.6522 | 0.7142 |
| 0.0418 | 25.0 | 16575 | 0.5825 | 0.83 | 0.6523 | 0.7141 |
| 0.0391 | 26.0 | 17238 | 0.5997 | 0.8306 | 0.6545 | 0.716 |
| 0.0376 | 27.0 | 17901 | 0.5894 | 0.8315 | 0.6546 | 0.7164 |
| 0.035 | 28.0 | 18564 | 0.6045 | 0.8306 | 0.6529 | 0.7149 |
| 0.0316 | 29.0 | 19227 | 0.6168 | 0.8311 | 0.6546 | 0.7162 |
| 0.0314 | 30.0 | 19890 | 0.6203 | 0.8311 | 0.6552 | 0.7164 |
| 0.0292 | 31.0 | 20553 | 0.6173 | 0.8315 | 0.6548 | 0.7163 |
| 0.0265 | 32.0 | 21216 | 0.6226 | 0.832 | 0.6548 | 0.7166 |
| 0.0274 | 33.0 | 21879 | 0.6264 | 0.8314 | 0.6538 | 0.7155 |
| 0.0247 | 34.0 | 22542 | 0.6254 | 0.8289 | 0.6515 | 0.7132 |
| 0.0238 | 35.0 | 23205 | 0.6254 | 0.8307 | 0.6519 | 0.7142 |
| 0.0232 | 36.0 | 23868 | 0.6295 | 0.8287 | 0.6515 | 0.7133 |
| 0.0215 | 37.0 | 24531 | 0.6326 | 0.8293 | 0.6523 | 0.7138 |
| 0.0212 | 38.0 | 25194 | 0.6332 | 0.8295 | 0.6522 | 0.714 |
| 0.0221 | 39.0 | 25857 | 0.6335 | 0.8305 | 0.6528 | 0.7147 |
| 0.0202 | 40.0 | 26520 | 0.6340 | 0.83 | 0.6526 | 0.7144 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrase-pubmed", "results": []}]}
|
text2text-generation
|
gayanin/bart-paraphrase-pubmed
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bart-paraphrase-pubmed
======================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6340
* Rouge2 Precision: 0.83
* Rouge2 Recall: 0.6526
* Rouge2 Fmeasure: 0.7144
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
57,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6131
- Rouge2 Precision: 0.3
- Rouge2 Recall: 0.2152
- Rouge2 Fmeasure: 0.2379
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.1335 | 1.0 | 563 | 1.7632 | 0.2716 | 0.1936 | 0.2135 |
| 1.9373 | 2.0 | 1126 | 1.7037 | 0.2839 | 0.2068 | 0.2265 |
| 1.8827 | 3.0 | 1689 | 1.6723 | 0.2901 | 0.2118 | 0.2316 |
| 1.8257 | 4.0 | 2252 | 1.6503 | 0.2938 | 0.2115 | 0.2332 |
| 1.8152 | 5.0 | 2815 | 1.6386 | 0.2962 | 0.2139 | 0.2357 |
| 1.7939 | 6.0 | 3378 | 1.6284 | 0.2976 | 0.212 | 0.2354 |
| 1.7845 | 7.0 | 3941 | 1.6211 | 0.2991 | 0.2155 | 0.2383 |
| 1.7468 | 8.0 | 4504 | 1.6167 | 0.2994 | 0.217 | 0.239 |
| 1.7464 | 9.0 | 5067 | 1.6137 | 0.3007 | 0.2154 | 0.2382 |
| 1.744 | 10.0 | 5630 | 1.6131 | 0.3 | 0.2152 | 0.2379 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-pubmed", "results": []}]}
|
text2text-generation
|
gayanin/t5-small-finetuned-pubmed
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-finetuned-pubmed
=========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6131
* Rouge2 Precision: 0.3
* Rouge2 Recall: 0.2152
* Rouge2 Fmeasure: 0.2379
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed-15
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5389
- Rouge2 Precision: 0.7165
- Rouge2 Recall: 0.5375
- Rouge2 Fmeasure: 0.5981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.1024 | 0.75 | 500 | 0.7890 | 0.6854 | 0.4813 | 0.5502 |
| 0.8788 | 1.51 | 1000 | 0.7176 | 0.6906 | 0.4989 | 0.5638 |
| 0.8086 | 2.26 | 1500 | 0.6830 | 0.6872 | 0.5052 | 0.5663 |
| 0.7818 | 3.02 | 2000 | 0.6650 | 0.6912 | 0.5104 | 0.5711 |
| 0.7466 | 3.77 | 2500 | 0.6458 | 0.6965 | 0.5167 | 0.5774 |
| 0.731 | 4.52 | 3000 | 0.6355 | 0.6955 | 0.5161 | 0.5763 |
| 0.7126 | 5.28 | 3500 | 0.6249 | 0.6924 | 0.517 | 0.576 |
| 0.6998 | 6.03 | 4000 | 0.6166 | 0.6995 | 0.5207 | 0.5809 |
| 0.6855 | 6.79 | 4500 | 0.6076 | 0.6981 | 0.5215 | 0.5813 |
| 0.676 | 7.54 | 5000 | 0.6015 | 0.7003 | 0.5242 | 0.5836 |
| 0.6688 | 8.3 | 5500 | 0.5962 | 0.7004 | 0.5235 | 0.583 |
| 0.6569 | 9.05 | 6000 | 0.5900 | 0.6997 | 0.5234 | 0.5827 |
| 0.6503 | 9.8 | 6500 | 0.5880 | 0.703 | 0.5257 | 0.5856 |
| 0.6455 | 10.56 | 7000 | 0.5818 | 0.7008 | 0.5259 | 0.5849 |
| 0.635 | 11.31 | 7500 | 0.5796 | 0.7017 | 0.5271 | 0.5861 |
| 0.6323 | 12.07 | 8000 | 0.5769 | 0.7053 | 0.5276 | 0.5877 |
| 0.6241 | 12.82 | 8500 | 0.5730 | 0.7011 | 0.5243 | 0.5838 |
| 0.6224 | 13.57 | 9000 | 0.5696 | 0.7046 | 0.5286 | 0.5879 |
| 0.6139 | 14.33 | 9500 | 0.5685 | 0.7047 | 0.5295 | 0.5886 |
| 0.6118 | 15.08 | 10000 | 0.5653 | 0.704 | 0.5297 | 0.5886 |
| 0.6089 | 15.84 | 10500 | 0.5633 | 0.703 | 0.5272 | 0.5865 |
| 0.598 | 16.59 | 11000 | 0.5613 | 0.7059 | 0.5293 | 0.5889 |
| 0.6003 | 17.35 | 11500 | 0.5602 | 0.7085 | 0.532 | 0.5918 |
| 0.5981 | 18.1 | 12000 | 0.5587 | 0.7106 | 0.5339 | 0.5938 |
| 0.5919 | 18.85 | 12500 | 0.5556 | 0.708 | 0.5319 | 0.5914 |
| 0.5897 | 19.61 | 13000 | 0.5556 | 0.7106 | 0.5327 | 0.5931 |
| 0.5899 | 20.36 | 13500 | 0.5526 | 0.7114 | 0.534 | 0.5939 |
| 0.5804 | 21.12 | 14000 | 0.5521 | 0.7105 | 0.5328 | 0.5928 |
| 0.5764 | 21.87 | 14500 | 0.5520 | 0.715 | 0.537 | 0.5976 |
| 0.5793 | 22.62 | 15000 | 0.5506 | 0.713 | 0.5346 | 0.5951 |
| 0.5796 | 23.38 | 15500 | 0.5492 | 0.7124 | 0.5352 | 0.5952 |
| 0.5672 | 24.13 | 16000 | 0.5482 | 0.7124 | 0.5346 | 0.5948 |
| 0.5737 | 24.89 | 16500 | 0.5470 | 0.7134 | 0.5352 | 0.5956 |
| 0.5685 | 25.64 | 17000 | 0.5463 | 0.7117 | 0.5346 | 0.5946 |
| 0.5658 | 26.4 | 17500 | 0.5457 | 0.7145 | 0.5359 | 0.5965 |
| 0.5657 | 27.15 | 18000 | 0.5447 | 0.7145 | 0.5367 | 0.597 |
| 0.5645 | 27.9 | 18500 | 0.5441 | 0.7141 | 0.5362 | 0.5964 |
| 0.565 | 28.66 | 19000 | 0.5436 | 0.7151 | 0.5367 | 0.5972 |
| 0.5579 | 29.41 | 19500 | 0.5426 | 0.7162 | 0.5378 | 0.5982 |
| 0.563 | 30.17 | 20000 | 0.5424 | 0.7155 | 0.5373 | 0.5977 |
| 0.556 | 30.92 | 20500 | 0.5418 | 0.7148 | 0.536 | 0.5966 |
| 0.5576 | 31.67 | 21000 | 0.5411 | 0.7141 | 0.5356 | 0.5961 |
| 0.5546 | 32.43 | 21500 | 0.5409 | 0.7149 | 0.5364 | 0.5967 |
| 0.556 | 33.18 | 22000 | 0.5405 | 0.7143 | 0.5356 | 0.596 |
| 0.5536 | 33.94 | 22500 | 0.5401 | 0.7165 | 0.5377 | 0.5982 |
| 0.5527 | 34.69 | 23000 | 0.5397 | 0.7188 | 0.5389 | 0.5999 |
| 0.5531 | 35.44 | 23500 | 0.5395 | 0.7172 | 0.538 | 0.5989 |
| 0.5508 | 36.2 | 24000 | 0.5392 | 0.7166 | 0.538 | 0.5985 |
| 0.5495 | 36.95 | 24500 | 0.5391 | 0.7176 | 0.5387 | 0.5993 |
| 0.5539 | 37.71 | 25000 | 0.5391 | 0.7169 | 0.5372 | 0.598 |
| 0.5452 | 38.46 | 25500 | 0.5390 | 0.7179 | 0.5384 | 0.5991 |
| 0.5513 | 39.22 | 26000 | 0.5390 | 0.717 | 0.5377 | 0.5984 |
| 0.5506 | 39.97 | 26500 | 0.5389 | 0.7165 | 0.5375 | 0.5981 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed-15", "results": []}]}
|
text2text-generation
|
gayanin/t5-small-mlm-pubmed-15
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-mlm-pubmed-15
======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5389
* Rouge2 Precision: 0.7165
* Rouge2 Recall: 0.5375
* Rouge2 Fmeasure: 0.5981
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed-35
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1101
- Rouge2 Precision: 0.4758
- Rouge2 Recall: 0.3498
- Rouge2 Fmeasure: 0.3927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.8404 | 0.75 | 500 | 1.5005 | 0.4265 | 0.2786 | 0.3273 |
| 1.6858 | 1.51 | 1000 | 1.4216 | 0.4318 | 0.2946 | 0.3404 |
| 1.6071 | 2.26 | 1500 | 1.3777 | 0.4472 | 0.3148 | 0.3598 |
| 1.5551 | 3.02 | 2000 | 1.3360 | 0.4406 | 0.3168 | 0.3586 |
| 1.5116 | 3.77 | 2500 | 1.3128 | 0.4523 | 0.3234 | 0.3671 |
| 1.4837 | 4.52 | 3000 | 1.2937 | 0.4477 | 0.3215 | 0.3645 |
| 1.4513 | 5.28 | 3500 | 1.2766 | 0.4511 | 0.3262 | 0.3689 |
| 1.4336 | 6.03 | 4000 | 1.2626 | 0.4548 | 0.3283 | 0.3718 |
| 1.4149 | 6.79 | 4500 | 1.2449 | 0.4495 | 0.3274 | 0.3687 |
| 1.3977 | 7.54 | 5000 | 1.2349 | 0.4507 | 0.3305 | 0.3712 |
| 1.3763 | 8.3 | 5500 | 1.2239 | 0.4519 | 0.3266 | 0.3688 |
| 1.371 | 9.05 | 6000 | 1.2171 | 0.4546 | 0.3305 | 0.3727 |
| 1.3501 | 9.8 | 6500 | 1.2080 | 0.4575 | 0.3329 | 0.3755 |
| 1.3443 | 10.56 | 7000 | 1.2017 | 0.4576 | 0.3314 | 0.3742 |
| 1.326 | 11.31 | 7500 | 1.1926 | 0.4578 | 0.333 | 0.3757 |
| 1.3231 | 12.07 | 8000 | 1.1866 | 0.4606 | 0.3357 | 0.3782 |
| 1.3089 | 12.82 | 8500 | 1.1816 | 0.4591 | 0.3338 | 0.3765 |
| 1.3007 | 13.57 | 9000 | 1.1764 | 0.4589 | 0.3361 | 0.3777 |
| 1.2943 | 14.33 | 9500 | 1.1717 | 0.4641 | 0.3382 | 0.3811 |
| 1.2854 | 15.08 | 10000 | 1.1655 | 0.4617 | 0.3378 | 0.38 |
| 1.2777 | 15.84 | 10500 | 1.1612 | 0.464 | 0.3401 | 0.3823 |
| 1.2684 | 16.59 | 11000 | 1.1581 | 0.4608 | 0.3367 | 0.3789 |
| 1.2612 | 17.35 | 11500 | 1.1554 | 0.4623 | 0.3402 | 0.3818 |
| 1.2625 | 18.1 | 12000 | 1.1497 | 0.4613 | 0.3381 | 0.3802 |
| 1.2529 | 18.85 | 12500 | 1.1465 | 0.4671 | 0.3419 | 0.3848 |
| 1.2461 | 19.61 | 13000 | 1.1431 | 0.4646 | 0.3399 | 0.3824 |
| 1.2415 | 20.36 | 13500 | 1.1419 | 0.4659 | 0.341 | 0.3835 |
| 1.2375 | 21.12 | 14000 | 1.1377 | 0.4693 | 0.3447 | 0.3873 |
| 1.2315 | 21.87 | 14500 | 1.1353 | 0.4672 | 0.3433 | 0.3855 |
| 1.2263 | 22.62 | 15000 | 1.1333 | 0.467 | 0.3433 | 0.3854 |
| 1.2214 | 23.38 | 15500 | 1.1305 | 0.4682 | 0.3446 | 0.3869 |
| 1.2202 | 24.13 | 16000 | 1.1291 | 0.4703 | 0.3465 | 0.3888 |
| 1.2155 | 24.89 | 16500 | 1.1270 | 0.472 | 0.348 | 0.3903 |
| 1.2064 | 25.64 | 17000 | 1.1261 | 0.4724 | 0.3479 | 0.3905 |
| 1.2173 | 26.4 | 17500 | 1.1236 | 0.4734 | 0.3485 | 0.3912 |
| 1.1994 | 27.15 | 18000 | 1.1220 | 0.4739 | 0.3486 | 0.3915 |
| 1.2018 | 27.9 | 18500 | 1.1217 | 0.4747 | 0.3489 | 0.3921 |
| 1.2045 | 28.66 | 19000 | 1.1194 | 0.4735 | 0.3488 | 0.3916 |
| 1.1949 | 29.41 | 19500 | 1.1182 | 0.4732 | 0.3484 | 0.3911 |
| 1.19 | 30.17 | 20000 | 1.1166 | 0.4724 | 0.3479 | 0.3904 |
| 1.1932 | 30.92 | 20500 | 1.1164 | 0.4753 | 0.3494 | 0.3924 |
| 1.1952 | 31.67 | 21000 | 1.1147 | 0.4733 | 0.3485 | 0.3911 |
| 1.1922 | 32.43 | 21500 | 1.1146 | 0.475 | 0.3494 | 0.3923 |
| 1.1889 | 33.18 | 22000 | 1.1132 | 0.4765 | 0.3499 | 0.3933 |
| 1.1836 | 33.94 | 22500 | 1.1131 | 0.4768 | 0.351 | 0.3939 |
| 1.191 | 34.69 | 23000 | 1.1127 | 0.4755 | 0.3495 | 0.3926 |
| 1.1811 | 35.44 | 23500 | 1.1113 | 0.4748 | 0.349 | 0.3919 |
| 1.1864 | 36.2 | 24000 | 1.1107 | 0.4751 | 0.3494 | 0.3921 |
| 1.1789 | 36.95 | 24500 | 1.1103 | 0.4756 | 0.3499 | 0.3927 |
| 1.1819 | 37.71 | 25000 | 1.1101 | 0.4758 | 0.35 | 0.3932 |
| 1.1862 | 38.46 | 25500 | 1.1099 | 0.4755 | 0.3497 | 0.3926 |
| 1.1764 | 39.22 | 26000 | 1.1101 | 0.4759 | 0.3498 | 0.3928 |
| 1.1819 | 39.97 | 26500 | 1.1101 | 0.4758 | 0.3498 | 0.3927 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed-35", "results": []}]}
|
text2text-generation
|
gayanin/t5-small-mlm-pubmed-35
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-mlm-pubmed-35
======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1101
* Rouge2 Precision: 0.4758
* Rouge2 Recall: 0.3498
* Rouge2 Fmeasure: 0.3927
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed-45
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6395
- Rouge2 Precision: 0.3383
- Rouge2 Recall: 0.2424
- Rouge2 Fmeasure: 0.2753
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.519 | 0.75 | 500 | 1.9659 | 0.3178 | 0.1888 | 0.2299 |
| 2.169 | 1.51 | 1000 | 1.8450 | 0.3256 | 0.2138 | 0.25 |
| 2.0796 | 2.26 | 1500 | 1.7900 | 0.3368 | 0.2265 | 0.2636 |
| 1.9978 | 3.02 | 2000 | 1.7553 | 0.3427 | 0.234 | 0.2709 |
| 1.9686 | 3.77 | 2500 | 1.7172 | 0.3356 | 0.2347 | 0.2692 |
| 1.9142 | 4.52 | 3000 | 1.6986 | 0.3358 | 0.238 | 0.2715 |
| 1.921 | 5.28 | 3500 | 1.6770 | 0.3349 | 0.2379 | 0.2709 |
| 1.8848 | 6.03 | 4000 | 1.6683 | 0.3346 | 0.2379 | 0.2708 |
| 1.8674 | 6.79 | 4500 | 1.6606 | 0.3388 | 0.2419 | 0.2752 |
| 1.8606 | 7.54 | 5000 | 1.6514 | 0.3379 | 0.2409 | 0.274 |
| 1.8515 | 8.3 | 5500 | 1.6438 | 0.3356 | 0.2407 | 0.2731 |
| 1.8403 | 9.05 | 6000 | 1.6401 | 0.3367 | 0.2421 | 0.2744 |
| 1.8411 | 9.8 | 6500 | 1.6395 | 0.3383 | 0.2424 | 0.2753 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed-45", "results": []}]}
|
text2text-generation
|
gayanin/t5-small-mlm-pubmed-45
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-mlm-pubmed-45
======================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6395
* Rouge2 Precision: 0.3383
* Rouge2 Recall: 0.2424
* Rouge2 Fmeasure: 0.2753
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-mlm-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8008
- Rouge2 Precision: 0.6071
- Rouge2 Recall: 0.4566
- Rouge2 Fmeasure: 0.5079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.914 | 0.75 | 500 | 0.8691 | 0.5901 | 0.4357 | 0.4879 |
| 0.9093 | 1.51 | 1000 | 0.8646 | 0.5867 | 0.4372 | 0.488 |
| 0.895 | 2.26 | 1500 | 0.8618 | 0.5891 | 0.4387 | 0.49 |
| 0.8842 | 3.02 | 2000 | 0.8571 | 0.5899 | 0.4374 | 0.4891 |
| 0.8796 | 3.77 | 2500 | 0.8544 | 0.5903 | 0.4406 | 0.4916 |
| 0.8759 | 4.52 | 3000 | 0.8513 | 0.5921 | 0.4395 | 0.4912 |
| 0.8621 | 5.28 | 3500 | 0.8485 | 0.5934 | 0.4413 | 0.493 |
| 0.8613 | 6.03 | 4000 | 0.8442 | 0.5944 | 0.4428 | 0.4944 |
| 0.8537 | 6.79 | 4500 | 0.8406 | 0.594 | 0.4414 | 0.4932 |
| 0.8518 | 7.54 | 5000 | 0.8399 | 0.5956 | 0.4424 | 0.4945 |
| 0.8438 | 8.3 | 5500 | 0.8365 | 0.5953 | 0.4452 | 0.4964 |
| 0.8339 | 9.05 | 6000 | 0.8353 | 0.5983 | 0.4468 | 0.4983 |
| 0.8307 | 9.8 | 6500 | 0.8331 | 0.5979 | 0.4461 | 0.4976 |
| 0.8328 | 10.56 | 7000 | 0.8304 | 0.5975 | 0.4465 | 0.4979 |
| 0.8263 | 11.31 | 7500 | 0.8283 | 0.5977 | 0.4467 | 0.4981 |
| 0.8168 | 12.07 | 8000 | 0.8267 | 0.5971 | 0.4463 | 0.4976 |
| 0.8165 | 12.82 | 8500 | 0.8248 | 0.5969 | 0.4462 | 0.4976 |
| 0.8084 | 13.57 | 9000 | 0.8245 | 0.6018 | 0.4527 | 0.5035 |
| 0.8136 | 14.33 | 9500 | 0.8219 | 0.6023 | 0.4509 | 0.5023 |
| 0.8073 | 15.08 | 10000 | 0.8206 | 0.6002 | 0.4486 | 0.5001 |
| 0.808 | 15.84 | 10500 | 0.8185 | 0.6009 | 0.4506 | 0.5019 |
| 0.8027 | 16.59 | 11000 | 0.8173 | 0.5978 | 0.4478 | 0.4989 |
| 0.8061 | 17.35 | 11500 | 0.8169 | 0.6022 | 0.4513 | 0.5026 |
| 0.7922 | 18.1 | 12000 | 0.8152 | 0.6016 | 0.4501 | 0.5016 |
| 0.7928 | 18.85 | 12500 | 0.8141 | 0.6009 | 0.45 | 0.5012 |
| 0.7909 | 19.61 | 13000 | 0.8143 | 0.6019 | 0.4521 | 0.5028 |
| 0.7909 | 20.36 | 13500 | 0.8115 | 0.5997 | 0.4505 | 0.5011 |
| 0.7949 | 21.12 | 14000 | 0.8115 | 0.6043 | 0.4536 | 0.5048 |
| 0.7853 | 21.87 | 14500 | 0.8095 | 0.6033 | 0.4527 | 0.5038 |
| 0.7819 | 22.62 | 15000 | 0.8095 | 0.6054 | 0.4541 | 0.5056 |
| 0.7828 | 23.38 | 15500 | 0.8075 | 0.6036 | 0.453 | 0.5042 |
| 0.787 | 24.13 | 16000 | 0.8068 | 0.6031 | 0.4528 | 0.504 |
| 0.7739 | 24.89 | 16500 | 0.8072 | 0.6043 | 0.4529 | 0.5045 |
| 0.7782 | 25.64 | 17000 | 0.8073 | 0.606 | 0.4551 | 0.5063 |
| 0.7772 | 26.4 | 17500 | 0.8063 | 0.6055 | 0.4549 | 0.5062 |
| 0.7718 | 27.15 | 18000 | 0.8057 | 0.606 | 0.4546 | 0.5059 |
| 0.7747 | 27.9 | 18500 | 0.8045 | 0.6046 | 0.4543 | 0.5054 |
| 0.7738 | 28.66 | 19000 | 0.8035 | 0.6059 | 0.4549 | 0.506 |
| 0.7642 | 29.41 | 19500 | 0.8041 | 0.6053 | 0.4545 | 0.5058 |
| 0.7666 | 30.17 | 20000 | 0.8039 | 0.6066 | 0.457 | 0.508 |
| 0.7686 | 30.92 | 20500 | 0.8027 | 0.6075 | 0.4571 | 0.5081 |
| 0.7664 | 31.67 | 21000 | 0.8026 | 0.6062 | 0.4566 | 0.5076 |
| 0.77 | 32.43 | 21500 | 0.8022 | 0.6068 | 0.4571 | 0.5081 |
| 0.7618 | 33.18 | 22000 | 0.8015 | 0.6065 | 0.4563 | 0.5072 |
| 0.7615 | 33.94 | 22500 | 0.8013 | 0.6064 | 0.4565 | 0.5074 |
| 0.7611 | 34.69 | 23000 | 0.8017 | 0.607 | 0.4567 | 0.5078 |
| 0.7611 | 35.44 | 23500 | 0.8013 | 0.608 | 0.4565 | 0.5082 |
| 0.7604 | 36.2 | 24000 | 0.8012 | 0.6069 | 0.4561 | 0.5072 |
| 0.7599 | 36.95 | 24500 | 0.8013 | 0.6078 | 0.4571 | 0.5085 |
| 0.7542 | 37.71 | 25000 | 0.8016 | 0.6083 | 0.4579 | 0.5091 |
| 0.7637 | 38.46 | 25500 | 0.8009 | 0.6072 | 0.4569 | 0.5081 |
| 0.7596 | 39.22 | 26000 | 0.8008 | 0.6069 | 0.4566 | 0.5078 |
| 0.7604 | 39.97 | 26500 | 0.8008 | 0.6071 | 0.4566 | 0.5079 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-mlm-pubmed", "results": []}]}
|
text2text-generation
|
gayanin/t5-small-mlm-pubmed
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-mlm-pubmed
===================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8008
* Rouge2 Precision: 0.6071
* Rouge2 Recall: 0.4566
* Rouge2 Fmeasure: 0.5079
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-paraphrase-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4032
- Rouge2 Precision: 0.8281
- Rouge2 Recall: 0.6346
- Rouge2 Fmeasure: 0.6996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.5253 | 1.0 | 663 | 0.4895 | 0.8217 | 0.6309 | 0.695 |
| 0.5385 | 2.0 | 1326 | 0.4719 | 0.822 | 0.6307 | 0.6953 |
| 0.5255 | 3.0 | 1989 | 0.4579 | 0.8225 | 0.631 | 0.6954 |
| 0.4927 | 4.0 | 2652 | 0.4510 | 0.824 | 0.6315 | 0.6965 |
| 0.484 | 5.0 | 3315 | 0.4426 | 0.8254 | 0.6323 | 0.6974 |
| 0.4691 | 6.0 | 3978 | 0.4383 | 0.8241 | 0.6311 | 0.6962 |
| 0.4546 | 7.0 | 4641 | 0.4319 | 0.8248 | 0.6322 | 0.6969 |
| 0.4431 | 8.0 | 5304 | 0.4270 | 0.8254 | 0.633 | 0.6977 |
| 0.4548 | 9.0 | 5967 | 0.4257 | 0.8257 | 0.6322 | 0.6976 |
| 0.4335 | 10.0 | 6630 | 0.4241 | 0.8271 | 0.6333 | 0.6986 |
| 0.4234 | 11.0 | 7293 | 0.4203 | 0.827 | 0.6341 | 0.6992 |
| 0.433 | 12.0 | 7956 | 0.4185 | 0.8279 | 0.6347 | 0.6998 |
| 0.4108 | 13.0 | 8619 | 0.4161 | 0.8285 | 0.6352 | 0.7004 |
| 0.4101 | 14.0 | 9282 | 0.4133 | 0.8289 | 0.6356 | 0.7008 |
| 0.4155 | 15.0 | 9945 | 0.4149 | 0.8279 | 0.635 | 0.6998 |
| 0.3991 | 16.0 | 10608 | 0.4124 | 0.8289 | 0.6353 | 0.7005 |
| 0.3962 | 17.0 | 11271 | 0.4113 | 0.829 | 0.6353 | 0.7006 |
| 0.3968 | 18.0 | 11934 | 0.4114 | 0.8285 | 0.6352 | 0.7002 |
| 0.3962 | 19.0 | 12597 | 0.4100 | 0.8282 | 0.6346 | 0.6998 |
| 0.3771 | 20.0 | 13260 | 0.4078 | 0.829 | 0.6352 | 0.7005 |
| 0.3902 | 21.0 | 13923 | 0.4083 | 0.8295 | 0.6351 | 0.7006 |
| 0.3811 | 22.0 | 14586 | 0.4077 | 0.8276 | 0.6346 | 0.6995 |
| 0.38 | 23.0 | 15249 | 0.4076 | 0.8281 | 0.6346 | 0.6997 |
| 0.3695 | 24.0 | 15912 | 0.4059 | 0.8277 | 0.6344 | 0.6993 |
| 0.3665 | 25.0 | 16575 | 0.4043 | 0.8278 | 0.6343 | 0.6992 |
| 0.3728 | 26.0 | 17238 | 0.4059 | 0.8279 | 0.6346 | 0.6994 |
| 0.3669 | 27.0 | 17901 | 0.4048 | 0.8271 | 0.6342 | 0.6991 |
| 0.3702 | 28.0 | 18564 | 0.4058 | 0.8265 | 0.6338 | 0.6985 |
| 0.3674 | 29.0 | 19227 | 0.4049 | 0.8277 | 0.6345 | 0.6993 |
| 0.364 | 30.0 | 19890 | 0.4048 | 0.8273 | 0.6341 | 0.699 |
| 0.3618 | 31.0 | 20553 | 0.4041 | 0.828 | 0.6349 | 0.6997 |
| 0.3609 | 32.0 | 21216 | 0.4040 | 0.8275 | 0.6346 | 0.6994 |
| 0.357 | 33.0 | 21879 | 0.4037 | 0.8278 | 0.6348 | 0.6996 |
| 0.3638 | 34.0 | 22542 | 0.4038 | 0.8275 | 0.634 | 0.6989 |
| 0.3551 | 35.0 | 23205 | 0.4035 | 0.8275 | 0.6344 | 0.6992 |
| 0.358 | 36.0 | 23868 | 0.4035 | 0.8279 | 0.6347 | 0.6995 |
| 0.3519 | 37.0 | 24531 | 0.4034 | 0.8277 | 0.6343 | 0.6992 |
| 0.359 | 38.0 | 25194 | 0.4035 | 0.8281 | 0.6346 | 0.6996 |
| 0.3542 | 39.0 | 25857 | 0.4033 | 0.8281 | 0.6346 | 0.6996 |
| 0.3592 | 40.0 | 26520 | 0.4032 | 0.8281 | 0.6346 | 0.6996 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-paraphrase-pubmed", "results": []}]}
|
text2text-generation
|
gayanin/t5-small-paraphrase-pubmed
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-paraphrase-pubmed
==========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4032
* Rouge2 Precision: 0.8281
* Rouge2 Recall: 0.6346
* Rouge2 Fmeasure: 0.6996
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2180
- Accuracy: 0.923
- F1: 0.9233
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8217 | 1.0 | 250 | 0.3137 | 0.903 | 0.8999 |
| 0.2484 | 2.0 | 500 | 0.2180 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.923, "name": "Accuracy"}, {"type": "f1", "value": 0.9233262687967644, "name": "F1"}]}]}]}
|
text-classification
|
gbade786/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2180
* Accuracy: 0.923
* F1: 0.9233
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 483413089
- CO2 Emissions (in grams): 210.6348731063569
## Validation Metrics
- Loss: 1.8478657007217407
- Rouge1: 50.5981
- Rouge2: 26.2167
- RougeL: 46.0513
- RougeLsum: 46.061
- Gen Len: 13.5987
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/gborn/autonlp-news-summarization-483413089
```
|
{"language": "en", "tags": "autonlp", "datasets": ["gborn/autonlp-data-news-summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 210.6348731063569}
|
text2text-generation
|
gborn/autonlp-news-summarization-483413089
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:gborn/autonlp-data-news-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-gborn/autonlp-data-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 483413089
- CO2 Emissions (in grams): 210.6348731063569
## Validation Metrics
- Loss: 1.8478657007217407
- Rouge1: 50.5981
- Rouge2: 26.2167
- RougeL: 46.0513
- RougeLsum: 46.061
- Gen Len: 13.5987
## Usage
You can use cURL to access this model:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 483413089\n- CO2 Emissions (in grams): 210.6348731063569",
"## Validation Metrics\n\n- Loss: 1.8478657007217407\n- Rouge1: 50.5981\n- Rouge2: 26.2167\n- RougeL: 46.0513\n- RougeLsum: 46.061\n- Gen Len: 13.5987",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-gborn/autonlp-data-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 483413089\n- CO2 Emissions (in grams): 210.6348731063569",
"## Validation Metrics\n\n- Loss: 1.8478657007217407\n- Rouge1: 50.5981\n- Rouge2: 26.2167\n- RougeL: 46.0513\n- RougeLsum: 46.061\n- Gen Len: 13.5987",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
73,
41,
55,
13
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-gborn/autonlp-data-news-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 483413089\n- CO2 Emissions (in grams): 210.6348731063569## Validation Metrics\n\n- Loss: 1.8478657007217407\n- Rouge1: 50.5981\n- Rouge2: 26.2167\n- RougeL: 46.0513\n- RougeLsum: 46.061\n- Gen Len: 13.5987## Usage\n\nYou can use cURL to access this model:"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6747
- Matthews Correlation: 0.5957
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4921 | 1.0 | 535 | 0.5283 | 0.5068 |
| 0.2837 | 2.0 | 1070 | 0.5133 | 0.5521 |
| 0.1775 | 3.0 | 1605 | 0.6747 | 0.5957 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-cased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5956649094312695, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-cola
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6747
* Matthews Correlation: 0.5957
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-mnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5721
- Accuracy: 0.8410
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5323 | 1.0 | 24544 | 0.4431 | 0.8302 |
| 0.3447 | 2.0 | 49088 | 0.4725 | 0.8353 |
| 0.2267 | 3.0 | 73632 | 0.5887 | 0.8368 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}, "metrics": [{"type": "accuracy", "value": 0.8410292921074044, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-mnli
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-mnli
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5721
* Accuracy: 0.8410
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7132
- Accuracy: 0.8603
- F1: 0.9026
- Combined Score: 0.8814
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5981 | 1.0 | 230 | 0.4580 | 0.7892 | 0.8562 | 0.8227 |
| 0.3739 | 2.0 | 460 | 0.3806 | 0.8480 | 0.8942 | 0.8711 |
| 0.1991 | 3.0 | 690 | 0.4879 | 0.8529 | 0.8958 | 0.8744 |
| 0.1286 | 4.0 | 920 | 0.6342 | 0.8529 | 0.8986 | 0.8758 |
| 0.0812 | 5.0 | 1150 | 0.7132 | 0.8603 | 0.9026 | 0.8814 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-cased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8602941176470589, "name": "Accuracy"}, {"type": "f1", "value": 0.9025641025641027, "name": "F1"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-mrpc
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-mrpc
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7132
* Accuracy: 0.8603
* F1: 0.9026
* Combined Score: 0.8814
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-qnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3986
- Accuracy: 0.9099
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.337 | 1.0 | 6547 | 0.9013 | 0.2448 |
| 0.1971 | 2.0 | 13094 | 0.9143 | 0.2839 |
| 0.1175 | 3.0 | 19641 | 0.9099 | 0.3986 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metrics": [{"type": "accuracy", "value": 0.9099395936298736, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-qnli
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-qnli
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3986
* Accuracy: 0.9099
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-qqp
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3752
- Accuracy: 0.9084
- F1: 0.8768
- Combined Score: 0.8926
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.308 | 1.0 | 22741 | 0.2548 | 0.8925 | 0.8556 | 0.8740 |
| 0.201 | 2.0 | 45482 | 0.2881 | 0.9032 | 0.8698 | 0.8865 |
| 0.1416 | 3.0 | 68223 | 0.3752 | 0.9084 | 0.8768 | 0.8926 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-cased-finetuned-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.9083848627256987, "name": "Accuracy"}, {"type": "f1", "value": 0.8767633750332712, "name": "F1"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-qqp
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-qqp
=============================
This model is a fine-tuned version of bert-base-cased on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3752
* Accuracy: 0.9084
* F1: 0.8768
* Combined Score: 0.8926
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-rte
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7260
- Accuracy: 0.6715
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6915 | 1.0 | 156 | 0.6491 | 0.6606 |
| 0.55 | 2.0 | 312 | 0.6737 | 0.6570 |
| 0.3955 | 3.0 | 468 | 0.7260 | 0.6715 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.6714801444043321, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-rte
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-rte
=============================
This model is a fine-tuned version of bert-base-cased on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7260
* Accuracy: 0.6715
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-sst2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3649
- Accuracy: 0.9232
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-sst2 \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.233 | 1.0 | 4210 | 0.9174 | 0.2841 |
| 0.1261 | 2.0 | 8420 | 0.9278 | 0.3310 |
| 0.0768 | 3.0 | 12630 | 0.9232 | 0.3649 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.9231651376146789, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-sst2
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-sst2
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3649
* Accuracy: 0.9232
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-stsb
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4861
- Pearson: 0.8926
- Spearmanr: 0.8898
- Combined Score: 0.8912
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:|
| 1.1174 | 1.0 | 360 | 0.8816 | 0.5000 | 0.8832 | 0.8800 |
| 0.3835 | 2.0 | 720 | 0.8901 | 0.4672 | 0.8915 | 0.8888 |
| 0.2388 | 3.0 | 1080 | 0.8912 | 0.4861 | 0.8926 | 0.8898 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "bert-base-cased-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "args": "stsb"}, "metrics": [{"type": "spearmanr", "value": 0.8897907271421561, "name": "Spearmanr"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-stsb
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-stsb
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4861
* Pearson: 0.8926
* Spearmanr: 0.8898
* Combined Score: 0.8912
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6996
- Accuracy: 0.4648
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7299 | 1.0 | 40 | 0.6923 | 0.5634 |
| 0.6982 | 2.0 | 80 | 0.7027 | 0.3803 |
| 0.6972 | 3.0 | 120 | 0.7005 | 0.4507 |
| 0.6992 | 4.0 | 160 | 0.6977 | 0.5352 |
| 0.699 | 5.0 | 200 | 0.6996 | 0.4648 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.4647887323943662, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-base-cased-finetuned-wnli
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-finetuned-wnli
==============================
This model is a fine-tuned version of bert-base-cased on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6996
* Accuracy: 0.4648
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
87,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-cola
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8385
- Matthews Correlation: 0.5957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5533 | 1.0 | 2138 | 0.7943 | 0.4439 |
| 0.5004 | 2.0 | 4276 | 0.7272 | 0.5678 |
| 0.2865 | 3.0 | 6414 | 0.8385 | 0.5957 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-large-cased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5957317644481708, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
gchhablani/bert-large-cased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-large-cased-finetuned-cola
===============================
This model is a fine-tuned version of bert-large-cased on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8385
* Matthews Correlation: 0.5957
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6274
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6441 | 1.0 | 917 | 0.6370 | 0.6838 | 0.8122 | 0.7480 |
| 0.6451 | 2.0 | 1834 | 0.6553 | 0.6838 | 0.8122 | 0.7480 |
| 0.6428 | 3.0 | 2751 | 0.6332 | 0.6838 | 0.8122 | 0.7480 |
| 0.6476 | 4.0 | 3668 | 0.6248 | 0.6838 | 0.8122 | 0.7480 |
| 0.6499 | 5.0 | 4585 | 0.6274 | 0.6838 | 0.8122 | 0.7480 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-large-cased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.6838235294117647, "name": "Accuracy"}, {"type": "f1", "value": 0.8122270742358079, "name": "F1"}]}]}]}
|
text-classification
|
gchhablani/bert-large-cased-finetuned-mrpc
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-large-cased-finetuned-mrpc
===============================
This model is a fine-tuned version of bert-large-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6274
* Accuracy: 0.6838
* F1: 0.8122
* Combined Score: 0.7480
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-rte
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5187
- Accuracy: 0.6643
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6969 | 1.0 | 623 | 0.7039 | 0.5343 |
| 0.5903 | 2.0 | 1246 | 0.6461 | 0.7184 |
| 0.4557 | 3.0 | 1869 | 1.5187 | 0.6643 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-cased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.6642599277978339, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-large-cased-finetuned-rte
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-large-cased-finetuned-rte
==============================
This model is a fine-tuned version of bert-large-cased on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5187
* Accuracy: 0.6643
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-wnli
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7087
- Accuracy: 0.3521
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.7114 | 1.0 | 159 | 0.5634 | 0.6923 |
| 0.7141 | 2.0 | 318 | 0.5634 | 0.6895 |
| 0.7063 | 3.0 | 477 | 0.5634 | 0.6930 |
| 0.712 | 4.0 | 636 | 0.4507 | 0.7077 |
| 0.7037 | 5.0 | 795 | 0.3521 | 0.7087 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-cased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.352112676056338, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/bert-large-cased-finetuned-wnli
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-large-cased-finetuned-wnli
===============================
This model is a fine-tuned version of bert-large-cased on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7087
* Accuracy: 0.3521
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fnet-base-finetuned-cola
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5929
- Matthews Correlation: 0.3594
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5895 | 1.0 | 535 | 0.6146 | 0.1699 |
| 0.4656 | 2.0 | 1070 | 0.5667 | 0.3047 |
| 0.3329 | 3.0 | 1605 | 0.5929 | 0.3594 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "fnet-base-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.35940659235571387, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
gchhablani/fnet-base-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
fnet-base-finetuned-cola
========================
This model is a fine-tuned version of google/fnet-base on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5929
* Matthews Correlation: 0.3594
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
88,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fnet-base-finetuned-mnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6443
- Accuracy: 0.7675
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7143 | 1.0 | 24544 | 0.6169 | 0.7504 |
| 0.5407 | 2.0 | 49088 | 0.6218 | 0.7627 |
| 0.4178 | 3.0 | 73632 | 0.6564 | 0.7658 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}, "metrics": [{"type": "accuracy", "value": 0.7674938974776241, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/fnet-base-finetuned-mnli
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
fnet-base-finetuned-mnli
========================
This model is a fine-tuned version of google/fnet-base on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6443
* Accuracy: 0.7675
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
88,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fnet-base-finetuned-mrpc
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9653
- Accuracy: 0.7721
- F1: 0.8502
- Combined Score: 0.8112
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.544 | 1.0 | 230 | 0.5272 | 0.7328 | 0.8300 | 0.7814 |
| 0.4034 | 2.0 | 460 | 0.6211 | 0.7255 | 0.8298 | 0.7776 |
| 0.2602 | 3.0 | 690 | 0.9110 | 0.7230 | 0.8306 | 0.7768 |
| 0.1688 | 4.0 | 920 | 0.8640 | 0.7696 | 0.8489 | 0.8092 |
| 0.0913 | 5.0 | 1150 | 0.9653 | 0.7721 | 0.8502 | 0.8112 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fnet-base-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.7720588235294118, "name": "Accuracy"}, {"type": "f1", "value": 0.8502415458937198, "name": "F1"}]}]}]}
|
text-classification
|
gchhablani/fnet-base-finetuned-mrpc
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
fnet-base-finetuned-mrpc
========================
This model is a fine-tuned version of google/fnet-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9653
* Accuracy: 0.7721
* F1: 0.8502
* Combined Score: 0.8112
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
88,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fnet-base-finetuned-qnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4746
- Accuracy: 0.8439
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4597 | 1.0 | 6547 | 0.3713 | 0.8411 |
| 0.3252 | 2.0 | 13094 | 0.3781 | 0.8420 |
| 0.2243 | 3.0 | 19641 | 0.4746 | 0.8439 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "fnet-base-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metrics": [{"type": "accuracy", "value": 0.8438586857038257, "name": "Accuracy"}]}]}]}
|
text-classification
|
gchhablani/fnet-base-finetuned-qnli
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
fnet-base-finetuned-qnli
========================
This model is a fine-tuned version of google/fnet-base on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4746
* Accuracy: 0.8439
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
88,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fnet-base-finetuned-qqp
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3686
- Accuracy: 0.8847
- F1: 0.8466
- Combined Score: 0.8657
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3484 | 1.0 | 22741 | 0.3014 | 0.8676 | 0.8297 | 0.8487 |
| 0.2387 | 2.0 | 45482 | 0.3011 | 0.8801 | 0.8429 | 0.8615 |
| 0.1739 | 3.0 | 68223 | 0.3686 | 0.8847 | 0.8466 | 0.8657 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fnet-base-finetuned-qqp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "metrics": [{"type": "accuracy", "value": 0.8847390551570616, "name": "Accuracy"}, {"type": "f1", "value": 0.8466197090382463, "name": "F1"}]}]}]}
|
text-classification
|
gchhablani/fnet-base-finetuned-qqp
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2105.03824"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
fnet-base-finetuned-qqp
=======================
This model is a fine-tuned version of google/fnet-base on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3686
* Accuracy: 0.8847
* F1: 0.8466
* Combined Score: 0.8657
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
This model is trained using the run\_glue script. The following command was used:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.0
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
88,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #fnet-bert-base-comparison #en #dataset-glue #arxiv-2105.03824 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.0\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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-0.011176830157637596,
-0.04266827553510666,
-0.253805011510849,
0.010820526629686356,
0.046582285314798355,
-0.017032980918884277,
-0.2275182157754898,
-0.1029951274394989,
-0.004221665672957897,
-0.06776802986860275,
-0.07437815517187119,
0.07514068484306335,
0.04294868931174278,
0.05498082563281059,
-0.06696301698684692,
0.028071772307157516,
-0.09679107367992401,
0.15699414908885956,
-0.15248030424118042,
-0.07556789368391037
] |
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