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transformers
|
# CLIP-Italian
CLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision.
We fine-tuned a competitive Italian CLIP model with only ~1.4 million Italian image-text pairs. This model is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Training Data
We considered three main sources of data:
- [WIT](https://github.com/google-research-datasets/wit)
- [MSCOCO-IT](https://github.com/crux82/mscoco-it)
- [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/)
## Training Procedure
Preprocessing, hardware used, hyperparameters...
## Evaluation Performance
## Limitations
## Usage
## Team members
- Federico Bianchi ([vinid](https://huggingface.co/vinid))
- Raphael Pisoni ([4rtemi5](https://huggingface.co/4rtemi5))
- Giuseppe Attanasio ([g8a9](https://huggingface.co/g8a9))
- Silvia Terragni ([silviatti](https://huggingface.co/silviatti))
- Dario Balestri ([D3Reo](https://huggingface.co/D3Reo))
- Gabriele Sarti ([gsarti](https://huggingface.co/gsarti))
- Sri Lakshmi ([srisweet](https://huggingface.co/srisweet))
## Useful links
- [CLIP Blog post](https://openai.com/blog/clip/)
- [CLIP paper](https://arxiv.org/abs/2103.00020)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Community Week channel](https://discord.com/channels/858019234139602994/859711887520038933)
- [Hybrid CLIP example scripts](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects/hybrid_clip)
- [Model Repository](https://huggingface.co/clip-italian/clip-italian-final/)
|
{"language": "it", "tags": ["italian", "bert", "vit", "vision"], "datasets": ["wit", "ctl/conceptualCaptions", "mscoco-it"]}
| null |
clip-italian/clip-italian-final
|
[
"transformers",
"jax",
"hybrid-clip",
"italian",
"bert",
"vit",
"vision",
"it",
"dataset:wit",
"dataset:ctl/conceptualCaptions",
"dataset:mscoco-it",
"arxiv:2103.00020",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2103.00020"
] |
[
"it"
] |
TAGS
#transformers #jax #hybrid-clip #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2103.00020 #endpoints_compatible #region-us
|
# CLIP-Italian
CLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision.
We fine-tuned a competitive Italian CLIP model with only ~1.4 million Italian image-text pairs. This model is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.
## Training Data
We considered three main sources of data:
- WIT
- MSCOCO-IT
- Conceptual Captions
## Training Procedure
Preprocessing, hardware used, hyperparameters...
## Evaluation Performance
## Limitations
## Usage
## Team members
- Federico Bianchi (vinid)
- Raphael Pisoni (4rtemi5)
- Giuseppe Attanasio (g8a9)
- Silvia Terragni (silviatti)
- Dario Balestri (D3Reo)
- Gabriele Sarti (gsarti)
- Sri Lakshmi (srisweet)
## Useful links
- CLIP Blog post
- CLIP paper
- Community Week README
- Community Week channel
- Hybrid CLIP example scripts
- Model Repository
|
[
"# CLIP-Italian\nCLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision. \n\nWe fine-tuned a competitive Italian CLIP model with only ~1.4 million Italian image-text pairs. This model is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.",
"## Training Data\nWe considered three main sources of data: \n- WIT\n- MSCOCO-IT\n- Conceptual Captions",
"## Training Procedure\nPreprocessing, hardware used, hyperparameters...",
"## Evaluation Performance",
"## Limitations",
"## Usage",
"## Team members\n- Federico Bianchi (vinid)\n- Raphael Pisoni (4rtemi5)\n- Giuseppe Attanasio (g8a9)\n- Silvia Terragni (silviatti)\n- Dario Balestri (D3Reo)\n- Gabriele Sarti (gsarti)\n- Sri Lakshmi (srisweet)",
"## Useful links\n- CLIP Blog post\n- CLIP paper\n- Community Week README\n- Community Week channel\n- Hybrid CLIP example scripts\n- Model Repository"
] |
[
"TAGS\n#transformers #jax #hybrid-clip #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2103.00020 #endpoints_compatible #region-us \n",
"# CLIP-Italian\nCLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision. \n\nWe fine-tuned a competitive Italian CLIP model with only ~1.4 million Italian image-text pairs. This model is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.",
"## Training Data\nWe considered three main sources of data: \n- WIT\n- MSCOCO-IT\n- Conceptual Captions",
"## Training Procedure\nPreprocessing, hardware used, hyperparameters...",
"## Evaluation Performance",
"## Limitations",
"## Usage",
"## Team members\n- Federico Bianchi (vinid)\n- Raphael Pisoni (4rtemi5)\n- Giuseppe Attanasio (g8a9)\n- Silvia Terragni (silviatti)\n- Dario Balestri (D3Reo)\n- Gabriele Sarti (gsarti)\n- Sri Lakshmi (srisweet)",
"## Useful links\n- CLIP Blog post\n- CLIP paper\n- Community Week README\n- Community Week channel\n- Hybrid CLIP example scripts\n- Model Repository"
] |
[
70,
111,
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3,
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34
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[
"passage: TAGS\n#transformers #jax #hybrid-clip #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2103.00020 #endpoints_compatible #region-us \n# CLIP-Italian\nCLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision. \n\nWe fine-tuned a competitive Italian CLIP model with only ~1.4 million Italian image-text pairs. This model is part of the Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.## Training Data\nWe considered three main sources of data: \n- WIT\n- MSCOCO-IT\n- Conceptual Captions## Training Procedure\nPreprocessing, hardware used, hyperparameters...## Evaluation Performance## Limitations## Usage## Team members\n- Federico Bianchi (vinid)\n- Raphael Pisoni (4rtemi5)\n- Giuseppe Attanasio (g8a9)\n- Silvia Terragni (silviatti)\n- Dario Balestri (D3Reo)\n- Gabriele Sarti (gsarti)\n- Sri Lakshmi (srisweet)## Useful links\n- CLIP Blog post\n- CLIP paper\n- Community Week README\n- Community Week channel\n- Hybrid CLIP example scripts\n- Model Repository"
] |
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null | null |
transformers
|
# Italian CLIP
Paper: [Contrastive Language-Image Pre-training for the Italian Language](https://arxiv.org/abs/2108.08688)
With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with **only 1.4 million** training samples. Our Italian CLIP model is built upon the [Italian BERT](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) model provided by [dbmdz](https://huggingface.co/dbmdz) and the OpenAI [vision transformer](https://huggingface.co/openai/clip-vit-base-patch32).
Do you want to test our model right away? We got you covered! You just need to head to our [demo application](https://huggingface.co/spaces/clip-italian/clip-italian-demo).
The demo also contains all the details of the project, from training tricks to our most impressive results, and much more!
# Training data
We considered four main sources of data:
+ [WIT](https://github.com/google-research-datasets/wit) is an image-caption dataset collected from Wikipedia (see,
[Srinivasan et al., 2021](https://arxiv.org/pdf/2103.01913.pdf)).
+ [MSCOCO-IT](https://github.com/crux82/mscoco-it). This image-caption dataset comes from the work by [Scaiella et al., 2019](http://www.ai-lc.it/IJCoL/v5n2/IJCOL_5_2_3___scaiella_et_al.pdf).
+ [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/). This image-caption dataset comes from
the work by [Sharma et al., 2018](https://aclanthology.org/P18-1238.pdf).
+ [La Foto del Giorno](https://www.ilpost.it/foto-del-giorno/). This image-caption dataset is collected from [Il Post](https://www.ilpost.it/), a prominent Italian online newspaper.
We used better data augmentation, strategic training choices (we have way less data than the original CLIP paper), and backbone-freezing pre-training. For all the details on that, please refer to our [demo](https://huggingface.co/spaces/clip-italian/clip-italian-demo).
# Experiments
## Quantitative Evaluation
To better understand how well our clip-italian model works we run an experimental evaluation. Since this is the first clip-based model in Italian, we used the multilingual CLIP model as a comparison baseline.
### mCLIP
The multilingual CLIP (henceforth, mCLIP), is a model introduced by [Nils Reimers](https://www.sbert.net/docs/pretrained_models.html) in his
[sentence-transformer](https://www.sbert.net/index.html) library. mCLIP is based on a multilingual encoder
that was created through multilingual knowledge distillation (see [Reimers et al., 2020](https://aclanthology.org/2020.emnlp-main.365/)).
### Tasks
We selected two different tasks:
+ image-retrieval
+ zero-shot classification
### Reproducibiliy
Both experiments should be very easy to replicate, we share the two colab notebook we used to compute the two results
+ [Image Retrieval](https://colab.research.google.com/drive/1bLVwVKpAndpEDHqjzxVPr_9nGrSbuOQd?usp=sharing)
+ [ImageNet Zero Shot Evaluation](https://colab.research.google.com/drive/1zfWeVWY79XXH63Ci-pk8xxx3Vu_RRgW-?usp=sharing)
### Image Retrieval
This experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input
a caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics
we use the MRR@K.
| MRR | CLIP-Italian | mCLIP |
| --------------- | ------------ |-------|
| MRR@1 | **0.3797** | 0.2874|
| MRR@5 | **0.5039** | 0.3957|
| MRR@10 | **0.5204** | 0.4129|
It is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained
on 400million images (and some of them probably were from MSCOCO).
### Zero-shot image classification
This experiment replicates the original one run by OpenAI on zero-shot image classification on ImageNet.
To do this, we used DeepL to translate the image labels in ImageNet. We evaluate the models computing the accuracy at different levels.
| Accuracy | CLIP-Italian | mCLIP |
| --------------- | ------------ |-------|
| Accuracy@1 | **22.11** | 20.15 |
| Accuracy@5 | **43.69** | 36.57 |
| Accuracy@10 | **52.55** | 42.91 |
| Accuracy@100 | **81.08** | 67.11 |
Our results confirm that CLIP-Italian is very competitive and beats mCLIP on the two different task
we have been testing. Note, however, that our results are lower than those shown in the original OpenAI
paper (see, [Radford et al., 2021](https://arxiv.org/abs/2103.00020)). However, considering that our results are in line with those obtained by mCLIP we think that
the translated image labels might have had an impact on the final scores.
# Team members
- Federico Bianchi ([vinid](https://huggingface.co/vinid))
- Raphael Pisoni ([4rtemi5](https://huggingface.co/4rtemi5))
- Giuseppe Attanasio ([g8a9](https://huggingface.co/g8a9))
- Silvia Terragni ([silviatti](https://huggingface.co/silviatti))
- Dario Balestri ([D3Reo](https://huggingface.co/D3Reo))
- Gabriele Sarti ([gsarti](https://huggingface.co/gsarti))
- Sri Lakshmi ([srisweet](https://huggingface.co/srisweet))
|
{"language": "it", "license": "gpl-3.0", "tags": ["italian", "bert", "vit", "vision"], "datasets": ["wit", "ctl/conceptualCaptions", "mscoco-it"]}
|
feature-extraction
|
clip-italian/clip-italian
|
[
"transformers",
"pytorch",
"jax",
"vision-text-dual-encoder",
"feature-extraction",
"italian",
"bert",
"vit",
"vision",
"it",
"dataset:wit",
"dataset:ctl/conceptualCaptions",
"dataset:mscoco-it",
"arxiv:2108.08688",
"arxiv:2103.01913",
"arxiv:2103.00020",
"license:gpl-3.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2108.08688",
"2103.01913",
"2103.00020"
] |
[
"it"
] |
TAGS
#transformers #pytorch #jax #vision-text-dual-encoder #feature-extraction #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2108.08688 #arxiv-2103.01913 #arxiv-2103.00020 #license-gpl-3.0 #endpoints_compatible #has_space #region-us
|
Italian CLIP
============
Paper: Contrastive Language-Image Pre-training for the Italian Language
With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with only 1.4 million training samples. Our Italian CLIP model is built upon the Italian BERT model provided by dbmdz and the OpenAI vision transformer.
Do you want to test our model right away? We got you covered! You just need to head to our demo application.
The demo also contains all the details of the project, from training tricks to our most impressive results, and much more!
Training data
=============
We considered four main sources of data:
* WIT is an image-caption dataset collected from Wikipedia (see,
Srinivasan et al., 2021).
* MSCOCO-IT. This image-caption dataset comes from the work by Scaiella et al., 2019.
* Conceptual Captions. This image-caption dataset comes from
the work by Sharma et al., 2018.
* La Foto del Giorno. This image-caption dataset is collected from Il Post, a prominent Italian online newspaper.
We used better data augmentation, strategic training choices (we have way less data than the original CLIP paper), and backbone-freezing pre-training. For all the details on that, please refer to our demo.
Experiments
===========
Quantitative Evaluation
-----------------------
To better understand how well our clip-italian model works we run an experimental evaluation. Since this is the first clip-based model in Italian, we used the multilingual CLIP model as a comparison baseline.
### mCLIP
The multilingual CLIP (henceforth, mCLIP), is a model introduced by Nils Reimers in his
sentence-transformer library. mCLIP is based on a multilingual encoder
that was created through multilingual knowledge distillation (see Reimers et al., 2020).
### Tasks
We selected two different tasks:
* image-retrieval
* zero-shot classification
### Reproducibiliy
Both experiments should be very easy to replicate, we share the two colab notebook we used to compute the two results
* Image Retrieval
* ImageNet Zero Shot Evaluation
### Image Retrieval
This experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input
a caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics
we use the MRR@K.
MRR: MRR@1, CLIP-Italian: 0.3797, mCLIP: 0.2874
MRR: MRR@5, CLIP-Italian: 0.5039, mCLIP: 0.3957
MRR: MRR@10, CLIP-Italian: 0.5204, mCLIP: 0.4129
It is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained
on 400million images (and some of them probably were from MSCOCO).
### Zero-shot image classification
This experiment replicates the original one run by OpenAI on zero-shot image classification on ImageNet.
To do this, we used DeepL to translate the image labels in ImageNet. We evaluate the models computing the accuracy at different levels.
Accuracy: Accuracy@1, CLIP-Italian: 22.11, mCLIP: 20.15
Accuracy: Accuracy@5, CLIP-Italian: 43.69, mCLIP: 36.57
Accuracy: Accuracy@10, CLIP-Italian: 52.55, mCLIP: 42.91
Accuracy: Accuracy@100, CLIP-Italian: 81.08, mCLIP: 67.11
Our results confirm that CLIP-Italian is very competitive and beats mCLIP on the two different task
we have been testing. Note, however, that our results are lower than those shown in the original OpenAI
paper (see, Radford et al., 2021). However, considering that our results are in line with those obtained by mCLIP we think that
the translated image labels might have had an impact on the final scores.
Team members
============
* Federico Bianchi (vinid)
* Raphael Pisoni (4rtemi5)
* Giuseppe Attanasio (g8a9)
* Silvia Terragni (silviatti)
* Dario Balestri (D3Reo)
* Gabriele Sarti (gsarti)
* Sri Lakshmi (srisweet)
|
[
"### mCLIP\n\n\nThe multilingual CLIP (henceforth, mCLIP), is a model introduced by Nils Reimers in his\nsentence-transformer library. mCLIP is based on a multilingual encoder\nthat was created through multilingual knowledge distillation (see Reimers et al., 2020).",
"### Tasks\n\n\nWe selected two different tasks:\n\n\n* image-retrieval\n* zero-shot classification",
"### Reproducibiliy\n\n\nBoth experiments should be very easy to replicate, we share the two colab notebook we used to compute the two results\n\n\n* Image Retrieval\n* ImageNet Zero Shot Evaluation",
"### Image Retrieval\n\n\nThis experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input\na caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics\nwe use the MRR@K.\n\n\nMRR: MRR@1, CLIP-Italian: 0.3797, mCLIP: 0.2874\nMRR: MRR@5, CLIP-Italian: 0.5039, mCLIP: 0.3957\nMRR: MRR@10, CLIP-Italian: 0.5204, mCLIP: 0.4129\n\n\nIt is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained\non 400million images (and some of them probably were from MSCOCO).",
"### Zero-shot image classification\n\n\nThis experiment replicates the original one run by OpenAI on zero-shot image classification on ImageNet.\nTo do this, we used DeepL to translate the image labels in ImageNet. We evaluate the models computing the accuracy at different levels.\n\n\nAccuracy: Accuracy@1, CLIP-Italian: 22.11, mCLIP: 20.15\nAccuracy: Accuracy@5, CLIP-Italian: 43.69, mCLIP: 36.57\nAccuracy: Accuracy@10, CLIP-Italian: 52.55, mCLIP: 42.91\nAccuracy: Accuracy@100, CLIP-Italian: 81.08, mCLIP: 67.11\n\n\nOur results confirm that CLIP-Italian is very competitive and beats mCLIP on the two different task\nwe have been testing. Note, however, that our results are lower than those shown in the original OpenAI\npaper (see, Radford et al., 2021). However, considering that our results are in line with those obtained by mCLIP we think that\nthe translated image labels might have had an impact on the final scores.\n\n\nTeam members\n============\n\n\n* Federico Bianchi (vinid)\n* Raphael Pisoni (4rtemi5)\n* Giuseppe Attanasio (g8a9)\n* Silvia Terragni (silviatti)\n* Dario Balestri (D3Reo)\n* Gabriele Sarti (gsarti)\n* Sri Lakshmi (srisweet)"
] |
[
"TAGS\n#transformers #pytorch #jax #vision-text-dual-encoder #feature-extraction #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2108.08688 #arxiv-2103.01913 #arxiv-2103.00020 #license-gpl-3.0 #endpoints_compatible #has_space #region-us \n",
"### mCLIP\n\n\nThe multilingual CLIP (henceforth, mCLIP), is a model introduced by Nils Reimers in his\nsentence-transformer library. mCLIP is based on a multilingual encoder\nthat was created through multilingual knowledge distillation (see Reimers et al., 2020).",
"### Tasks\n\n\nWe selected two different tasks:\n\n\n* image-retrieval\n* zero-shot classification",
"### Reproducibiliy\n\n\nBoth experiments should be very easy to replicate, we share the two colab notebook we used to compute the two results\n\n\n* Image Retrieval\n* ImageNet Zero Shot Evaluation",
"### Image Retrieval\n\n\nThis experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input\na caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics\nwe use the MRR@K.\n\n\nMRR: MRR@1, CLIP-Italian: 0.3797, mCLIP: 0.2874\nMRR: MRR@5, CLIP-Italian: 0.5039, mCLIP: 0.3957\nMRR: MRR@10, CLIP-Italian: 0.5204, mCLIP: 0.4129\n\n\nIt is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained\non 400million images (and some of them probably were from MSCOCO).",
"### Zero-shot image classification\n\n\nThis experiment replicates the original one run by OpenAI on zero-shot image classification on ImageNet.\nTo do this, we used DeepL to translate the image labels in ImageNet. We evaluate the models computing the accuracy at different levels.\n\n\nAccuracy: Accuracy@1, CLIP-Italian: 22.11, mCLIP: 20.15\nAccuracy: Accuracy@5, CLIP-Italian: 43.69, mCLIP: 36.57\nAccuracy: Accuracy@10, CLIP-Italian: 52.55, mCLIP: 42.91\nAccuracy: Accuracy@100, CLIP-Italian: 81.08, mCLIP: 67.11\n\n\nOur results confirm that CLIP-Italian is very competitive and beats mCLIP on the two different task\nwe have been testing. Note, however, that our results are lower than those shown in the original OpenAI\npaper (see, Radford et al., 2021). However, considering that our results are in line with those obtained by mCLIP we think that\nthe translated image labels might have had an impact on the final scores.\n\n\nTeam members\n============\n\n\n* Federico Bianchi (vinid)\n* Raphael Pisoni (4rtemi5)\n* Giuseppe Attanasio (g8a9)\n* Silvia Terragni (silviatti)\n* Dario Balestri (D3Reo)\n* Gabriele Sarti (gsarti)\n* Sri Lakshmi (srisweet)"
] |
[
114,
73,
23,
45,
189,
337
] |
[
"passage: TAGS\n#transformers #pytorch #jax #vision-text-dual-encoder #feature-extraction #italian #bert #vit #vision #it #dataset-wit #dataset-ctl/conceptualCaptions #dataset-mscoco-it #arxiv-2108.08688 #arxiv-2103.01913 #arxiv-2103.00020 #license-gpl-3.0 #endpoints_compatible #has_space #region-us \n### mCLIP\n\n\nThe multilingual CLIP (henceforth, mCLIP), is a model introduced by Nils Reimers in his\nsentence-transformer library. mCLIP is based on a multilingual encoder\nthat was created through multilingual knowledge distillation (see Reimers et al., 2020).### Tasks\n\n\nWe selected two different tasks:\n\n\n* image-retrieval\n* zero-shot classification### Reproducibiliy\n\n\nBoth experiments should be very easy to replicate, we share the two colab notebook we used to compute the two results\n\n\n* Image Retrieval\n* ImageNet Zero Shot Evaluation### Image Retrieval\n\n\nThis experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input\na caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics\nwe use the MRR@K.\n\n\nMRR: MRR@1, CLIP-Italian: 0.3797, mCLIP: 0.2874\nMRR: MRR@5, CLIP-Italian: 0.5039, mCLIP: 0.3957\nMRR: MRR@10, CLIP-Italian: 0.5204, mCLIP: 0.4129\n\n\nIt is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained\non 400million images (and some of them probably were from MSCOCO)."
] |
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null | null |
transformers
|
# CoNTACT
### Model description
<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or **CoNTACT** is a Dutch RobBERT model (```pdelobelle/robbert-v2-dutch-base```) adapted to the domain of COVID-19 tweets. The model was developed at [CLiPS](https://www.uantwerpen.be/en/research-groups/clips/) by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full description of the model, the data that was used and the experiments that were conducted can be found in this ArXiv preprint: https://arxiv.org/abs/2203.07362
### Intended use
The model was developed with the intention of achieving high results on NLP tasks involving Dutch social media messages related to COVID-19.
### How to use
CoNTACT should be fine-tuned on a downstream task. This can be achieved by referring to ```clips/contact``` in the ```--model_name_or_path``` argument in Huggingface/Transformers' example scripts, or by loading CoNTACT (as shown below) and fine-tuning it using your own code:
```
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('clips/contact')
tokenizer = AutoTokenizer.from_pretrained('clips/contact')
...
```
### Training data
CoNTACT was trained on 2.8M Dutch tweets related to COVID-19 that were posted in 2021.
### Training Procedure
The model's pre-training phase was extended by performing Masked Language Modeling (MLM) on the training data described above. This was done for 4 epochs, using the largest possible batch size that fit working memory (32).
### Evaluation
The model was evaluated on two tasks using data from two social media platforms: Twitter and Facebook. Task 1 involved the binary classification of COVID-19 vaccine stance (hesitant vs. not hesitant), whereas task 2 consisted of the mulilabel, multiclass classification of arguments for vaccine hesitancy. CoNTACT outperformed out-of-the-box RobBERT in virtually all our experiments, and with statistical significance in most cases.
### How to cite
```
@misc{lemmens2022contact,
title={CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection},
author={Jens Lemmens and Jens Van Nooten and Tim Kreutz and Walter Daelemans},
year={2022},
eprint={2203.07362},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{}
|
feature-extraction
|
clips/contact
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2203.07362",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2203.07362"
] |
[] |
TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07362 #endpoints_compatible #region-us
|
# CoNTACT
### Model description
<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full description of the model, the data that was used and the experiments that were conducted can be found in this ArXiv preprint: URL
### Intended use
The model was developed with the intention of achieving high results on NLP tasks involving Dutch social media messages related to COVID-19.
### How to use
CoNTACT should be fine-tuned on a downstream task. This can be achieved by referring to in the argument in Huggingface/Transformers' example scripts, or by loading CoNTACT (as shown below) and fine-tuning it using your own code:
### Training data
CoNTACT was trained on 2.8M Dutch tweets related to COVID-19 that were posted in 2021.
### Training Procedure
The model's pre-training phase was extended by performing Masked Language Modeling (MLM) on the training data described above. This was done for 4 epochs, using the largest possible batch size that fit working memory (32).
### Evaluation
The model was evaluated on two tasks using data from two social media platforms: Twitter and Facebook. Task 1 involved the binary classification of COVID-19 vaccine stance (hesitant vs. not hesitant), whereas task 2 consisted of the mulilabel, multiclass classification of arguments for vaccine hesitancy. CoNTACT outperformed out-of-the-box RobBERT in virtually all our experiments, and with statistical significance in most cases.
### How to cite
|
[
"# CoNTACT",
"### Model description\n\n<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full description of the model, the data that was used and the experiments that were conducted can be found in this ArXiv preprint: URL",
"### Intended use\n\nThe model was developed with the intention of achieving high results on NLP tasks involving Dutch social media messages related to COVID-19.",
"### How to use\n\nCoNTACT should be fine-tuned on a downstream task. This can be achieved by referring to in the argument in Huggingface/Transformers' example scripts, or by loading CoNTACT (as shown below) and fine-tuning it using your own code:",
"### Training data\n\nCoNTACT was trained on 2.8M Dutch tweets related to COVID-19 that were posted in 2021.",
"### Training Procedure\n\nThe model's pre-training phase was extended by performing Masked Language Modeling (MLM) on the training data described above. This was done for 4 epochs, using the largest possible batch size that fit working memory (32).",
"### Evaluation\n\nThe model was evaluated on two tasks using data from two social media platforms: Twitter and Facebook. Task 1 involved the binary classification of COVID-19 vaccine stance (hesitant vs. not hesitant), whereas task 2 consisted of the mulilabel, multiclass classification of arguments for vaccine hesitancy. CoNTACT outperformed out-of-the-box RobBERT in virtually all our experiments, and with statistical significance in most cases.",
"### How to cite"
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07362 #endpoints_compatible #region-us \n",
"# CoNTACT",
"### Model description\n\n<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full description of the model, the data that was used and the experiments that were conducted can be found in this ArXiv preprint: URL",
"### Intended use\n\nThe model was developed with the intention of achieving high results on NLP tasks involving Dutch social media messages related to COVID-19.",
"### How to use\n\nCoNTACT should be fine-tuned on a downstream task. This can be achieved by referring to in the argument in Huggingface/Transformers' example scripts, or by loading CoNTACT (as shown below) and fine-tuning it using your own code:",
"### Training data\n\nCoNTACT was trained on 2.8M Dutch tweets related to COVID-19 that were posted in 2021.",
"### Training Procedure\n\nThe model's pre-training phase was extended by performing Masked Language Modeling (MLM) on the training data described above. This was done for 4 epochs, using the largest possible batch size that fit working memory (32).",
"### Evaluation\n\nThe model was evaluated on two tasks using data from two social media platforms: Twitter and Facebook. Task 1 involved the binary classification of COVID-19 vaccine stance (hesitant vs. not hesitant), whereas task 2 consisted of the mulilabel, multiclass classification of arguments for vaccine hesitancy. CoNTACT outperformed out-of-the-box RobBERT in virtually all our experiments, and with statistical significance in most cases.",
"### How to cite"
] |
[
39,
4,
148,
37,
68,
28,
58,
114,
5
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2203.07362 #endpoints_compatible #region-us \n# CoNTACT### Model description\n\n<u>Co</u>ntextual <u>N</u>eural <u>T</u>ransformer <u>A</u>dapted to <u>C</u>OVID-19 <u>T</u>weets or CoNTACT is a Dutch RobBERT model () adapted to the domain of COVID-19 tweets. The model was developed at CLiPS by Jens Lemmens, Jens Van Nooten, Tim Kreutz and Walter Daelemans. A full description of the model, the data that was used and the experiments that were conducted can be found in this ArXiv preprint: URL### Intended use\n\nThe model was developed with the intention of achieving high results on NLP tasks involving Dutch social media messages related to COVID-19.### How to use\n\nCoNTACT should be fine-tuned on a downstream task. This can be achieved by referring to in the argument in Huggingface/Transformers' example scripts, or by loading CoNTACT (as shown below) and fine-tuning it using your own code:### Training data\n\nCoNTACT was trained on 2.8M Dutch tweets related to COVID-19 that were posted in 2021.### Training Procedure\n\nThe model's pre-training phase was extended by performing Masked Language Modeling (MLM) on the training data described above. This was done for 4 epochs, using the largest possible batch size that fit working memory (32).### Evaluation\n\nThe model was evaluated on two tasks using data from two social media platforms: Twitter and Facebook. Task 1 involved the binary classification of COVID-19 vaccine stance (hesitant vs. not hesitant), whereas task 2 consisted of the mulilabel, multiclass classification of arguments for vaccine hesitancy. CoNTACT outperformed out-of-the-box RobBERT in virtually all our experiments, and with statistical significance in most cases.### How to cite"
] |
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] |
null | null |
sentence-transformers
|
# MFAQ
We present a multilingual FAQ retrieval model trained on the [MFAQ dataset](https://huggingface.co/datasets/clips/mfaq), it ranks candidate answers according to a given question.
## Installation
```
pip install sentence-transformers transformers
```
## Usage
You can use MFAQ with sentence-transformers or directly with a HuggingFace model.
In both cases, questions need to be prepended with `<Q>`, and answers with `<A>`.
#### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
question = "<Q>How many models can I host on HuggingFace?"
answer_1 = "<A>All plans come with unlimited private models and datasets."
answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."
model = SentenceTransformer('clips/mfaq')
embeddings = model.encode([question, answer_1, answer_3, answer_3])
print(embeddings)
```
#### HuggingFace Transformers
```python
from transformers import AutoTokenizer, AutoModel
import torch
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)
question = "<Q>How many models can I host on HuggingFace?"
answer_1 = "<A>All plans come with unlimited private models and datasets."
answer_2 = "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem."
answer_3 = "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."
tokenizer = AutoTokenizer.from_pretrained('clips/mfaq')
model = AutoModel.from_pretrained('clips/mfaq')
# Tokenize sentences
encoded_input = tokenizer([question, answer_1, answer_3, answer_3], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
```
## Training
You can find the training script for the model [here](https://github.com/clips/mfaq).
## People
This model was developed by [Maxime De Bruyn](https://www.linkedin.com/in/maximedebruyn/), Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.
## Citation information
```
@misc{debruyn2021mfaq,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime De Bruyn and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
eprint={2109.12870},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": ["cs", "da", "de", "en", "es", "fi", "fr", "he", "hr", "hu", "id", "it", "nl", "no", "pl", "pt", "ro", "ru", "sv", "tr", "vi"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["clips/mfaq"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "<Q>How many models can I host on HuggingFace?", "sentences": ["<A>All plans come with unlimited private models and datasets.", "<A>AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem.", "<A>Based on how much training data and model variants are created, we send you a compute cost and payment link - as low as $10 per job."]}}
|
sentence-similarity
|
clips/mfaq
|
[
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"cs",
"da",
"de",
"en",
"es",
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"fr",
"he",
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"hu",
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"nl",
"no",
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"pt",
"ro",
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"vi",
"dataset:clips/mfaq",
"arxiv:2109.12870",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.12870"
] |
[
"cs",
"da",
"de",
"en",
"es",
"fi",
"fr",
"he",
"hr",
"hu",
"id",
"it",
"nl",
"no",
"pl",
"pt",
"ro",
"ru",
"sv",
"tr",
"vi"
] |
TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #cs #da #de #en #es #fi #fr #he #hr #hu #id #it #nl #no #pl #pt #ro #ru #sv #tr #vi #dataset-clips/mfaq #arxiv-2109.12870 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# MFAQ
We present a multilingual FAQ retrieval model trained on the MFAQ dataset, it ranks candidate answers according to a given question.
## Installation
## Usage
You can use MFAQ with sentence-transformers or directly with a HuggingFace model.
In both cases, questions need to be prepended with '<Q>', and answers with '<A>'.
#### Sentence Transformers
#### HuggingFace Transformers
## Training
You can find the training script for the model here.
## People
This model was developed by Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.
information
|
[
"# MFAQ\n\nWe present a multilingual FAQ retrieval model trained on the MFAQ dataset, it ranks candidate answers according to a given question.",
"## Installation",
"## Usage\nYou can use MFAQ with sentence-transformers or directly with a HuggingFace model. \nIn both cases, questions need to be prepended with '<Q>', and answers with '<A>'.",
"#### Sentence Transformers",
"#### HuggingFace Transformers",
"## Training\nYou can find the training script for the model here.",
"## People\nThis model was developed by Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.\n\ninformation"
] |
[
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #cs #da #de #en #es #fi #fr #he #hr #hu #id #it #nl #no #pl #pt #ro #ru #sv #tr #vi #dataset-clips/mfaq #arxiv-2109.12870 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# MFAQ\n\nWe present a multilingual FAQ retrieval model trained on the MFAQ dataset, it ranks candidate answers according to a given question.",
"## Installation",
"## Usage\nYou can use MFAQ with sentence-transformers or directly with a HuggingFace model. \nIn both cases, questions need to be prepended with '<Q>', and answers with '<A>'.",
"#### Sentence Transformers",
"#### HuggingFace Transformers",
"## Training\nYou can find the training script for the model here.",
"## People\nThis model was developed by Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.\n\ninformation"
] |
[
120,
36,
2,
51,
7,
9,
13,
30
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #cs #da #de #en #es #fi #fr #he #hr #hu #id #it #nl #no #pl #pt #ro #ru #sv #tr #vi #dataset-clips/mfaq #arxiv-2109.12870 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# MFAQ\n\nWe present a multilingual FAQ retrieval model trained on the MFAQ dataset, it ranks candidate answers according to a given question.## Installation## Usage\nYou can use MFAQ with sentence-transformers or directly with a HuggingFace model. \nIn both cases, questions need to be prepended with '<Q>', and answers with '<A>'.#### Sentence Transformers#### HuggingFace Transformers## Training\nYou can find the training script for the model here.## People\nThis model was developed by Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.\n\ninformation"
] |
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null | null |
transformers
|
## albert_chinese_small
### Overview
**Language model:** albert-small
**Model size:** 18.5M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
**NOTE:**Since sentencepiece is not used in `albert_chinese_small` model, you have to call **BertTokenizer** instead of AlbertTokenizer !!!
```
import torch
from transformers import BertTokenizer, AlbertModel
tokenizer = BertTokenizer.from_pretrained("clue/albert_chinese_small")
albert = AlbertModel.from_pretrained("clue/albert_chinese_small")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
{"language": "zh"}
| null |
clue/albert_chinese_small
|
[
"transformers",
"pytorch",
"albert",
"zh",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #albert #zh #endpoints_compatible #region-us
|
## albert_chinese_small
### Overview
Language model: albert-small
Model size: 18.5M
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE:Since sentencepiece is not used in 'albert_chinese_small' model, you have to call BertTokenizer instead of AlbertTokenizer !!!
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: URL
Website: URL
|
[
"## albert_chinese_small",
"### Overview\n\nLanguage model: albert-small\nModel size: 18.5M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE:Since sentencepiece is not used in 'albert_chinese_small' model, you have to call BertTokenizer instead of AlbertTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
"TAGS\n#transformers #pytorch #albert #zh #endpoints_compatible #region-us \n",
"## albert_chinese_small",
"### Overview\n\nLanguage model: albert-small\nModel size: 18.5M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE:Since sentencepiece is not used in 'albert_chinese_small' model, you have to call BertTokenizer instead of AlbertTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
26,
9,
38,
23,
41,
47
] |
[
"passage: TAGS\n#transformers #pytorch #albert #zh #endpoints_compatible #region-us \n## albert_chinese_small### Overview\n\nLanguage model: albert-small\nModel size: 18.5M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.### Usage\n\nNOTE:Since sentencepiece is not used in 'albert_chinese_small' model, you have to call BertTokenizer instead of AlbertTokenizer !!!### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
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] |
null | null |
transformers
|
## albert_chinese_tiny
### Overview
**Language model:** albert-tiny
**Model size:** 16M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
**NOTE:**Since sentencepiece is not used in `albert_chinese_tiny` model, you have to call **BertTokenizer** instead of AlbertTokenizer !!!
```
import torch
from transformers import BertTokenizer, AlbertModel
tokenizer = BertTokenizer.from_pretrained("clue/albert_chinese_tiny")
albert = AlbertModel.from_pretrained("clue/albert_chinese_tiny")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
{"language": "zh"}
| null |
clue/albert_chinese_tiny
|
[
"transformers",
"pytorch",
"albert",
"zh",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #albert #zh #endpoints_compatible #region-us
|
## albert_chinese_tiny
### Overview
Language model: albert-tiny
Model size: 16M
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE:Since sentencepiece is not used in 'albert_chinese_tiny' model, you have to call BertTokenizer instead of AlbertTokenizer !!!
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: URL
Website: URL
|
[
"## albert_chinese_tiny",
"### Overview\n\nLanguage model: albert-tiny\nModel size: 16M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE:Since sentencepiece is not used in 'albert_chinese_tiny' model, you have to call BertTokenizer instead of AlbertTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
"TAGS\n#transformers #pytorch #albert #zh #endpoints_compatible #region-us \n",
"## albert_chinese_tiny",
"### Overview\n\nLanguage model: albert-tiny\nModel size: 16M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE:Since sentencepiece is not used in 'albert_chinese_tiny' model, you have to call BertTokenizer instead of AlbertTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
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[
"passage: TAGS\n#transformers #pytorch #albert #zh #endpoints_compatible #region-us \n## albert_chinese_tiny### Overview\n\nLanguage model: albert-tiny\nModel size: 16M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.### Usage\n\nNOTE:Since sentencepiece is not used in 'albert_chinese_tiny' model, you have to call BertTokenizer instead of AlbertTokenizer !!!### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
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] |
null | null |
transformers
|
# Introduction
This model was trained on TPU and the details are as follows:
## Model
##
| Model_name | params | size | Training_corpus | Vocab |
| :------------------------------------------ | :----- | :------- | :----------------- | :-----------: |
| **`RoBERTa-tiny-clue`** <br/>Super_small_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny-pair`** <br/>Super_small_sentence_pair_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny3L768-clue`** <br/>small_model | 38M | 110M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny3L312-clue`** <br/>small_model | <7.5M | 24M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-large-clue`** <br/> Large_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-large-pair`** <br/>Large_sentence_pair_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** |
### Usage
With the help of[Huggingface-Transformers 2.5.1](https://github.com/huggingface/transformers), you could use these model as follows
```
tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
model = BertModel.from_pretrained("MODEL_NAME")
```
`MODEL_NAME`：
| Model_NAME | MODEL_LINK |
| -------------------------- | ------------------------------------------------------------ |
| **RoBERTa-tiny-clue** | [`clue/roberta_chinese_clue_tiny`](https://huggingface.co/clue/roberta_chinese_clue_tiny) |
| **RoBERTa-tiny-pair** | [`clue/roberta_chinese_pair_tiny`](https://huggingface.co/clue/roberta_chinese_pair_tiny) |
| **RoBERTa-tiny3L768-clue** | [`clue/roberta_chinese_3L768_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L768_clue_tiny) |
| **RoBERTa-tiny3L312-clue** | [`clue/roberta_chinese_3L312_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L312_clue_tiny) |
| **RoBERTa-large-clue** | [`clue/roberta_chinese_clue_large`](https://huggingface.co/clue/roberta_chinese_clue_large) |
| **RoBERTa-large-pair** | [`clue/roberta_chinese_pair_large`](https://huggingface.co/clue/roberta_chinese_pair_large) |
## Details
Please read <a href='https://arxiv.org/pdf/2003.01355'>https://arxiv.org/pdf/2003.01355.
Please visit our repository: https://github.com/CLUEbenchmark/CLUEPretrainedModels.git
|
{"language": "zh"}
| null |
clue/roberta_chinese_3L312_clue_tiny
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"arxiv:2003.01355",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2003.01355"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #jax #roberta #zh #arxiv-2003.01355 #endpoints_compatible #region-us
|
Introduction
============
This model was trained on TPU and the details are as follows:
Model
-----
### Usage
With the help ofHuggingface-Transformers 2.5.1, you could use these model as follows
'MODEL\_NAME'：
Details
-------
Please read <a href='URL/URL
Please visit our repository: URL
|
[
"### Usage\n\n\nWith the help ofHuggingface-Transformers 2.5.1, you could use these model as follows\n\n\n'MODEL\\_NAME'：\n\n\n\nDetails\n-------\n\n\nPlease read <a href='URL/URL\n\n\nPlease visit our repository: URL"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #zh #arxiv-2003.01355 #endpoints_compatible #region-us \n",
"### Usage\n\n\nWith the help ofHuggingface-Transformers 2.5.1, you could use these model as follows\n\n\n'MODEL\\_NAME'：\n\n\n\nDetails\n-------\n\n\nPlease read <a href='URL/URL\n\n\nPlease visit our repository: URL"
] |
[
38,
56
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #zh #arxiv-2003.01355 #endpoints_compatible #region-us \n### Usage\n\n\nWith the help ofHuggingface-Transformers 2.5.1, you could use these model as follows\n\n\n'MODEL\\_NAME'：\n\n\n\nDetails\n-------\n\n\nPlease read <a href='URL/URL\n\n\nPlease visit our repository: URL"
] |
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null | null |
transformers
|
## roberta_chinese_base
### Overview
**Language model:** roberta-base
**Model size:** 392M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
**NOTE:** You have to call **BertTokenizer** instead of RobertaTokenizer !!!
```
import torch
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("clue/roberta_chinese_base")
roberta = BertModel.from_pretrained("clue/roberta_chinese_base")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
{"language": "zh"}
| null |
clue/roberta_chinese_base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us
|
## roberta_chinese_base
### Overview
Language model: roberta-base
Model size: 392M
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: URL
Website: URL
|
[
"## roberta_chinese_base",
"### Overview\n\nLanguage model: roberta-base\nModel size: 392M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us \n",
"## roberta_chinese_base",
"### Overview\n\nLanguage model: roberta-base\nModel size: 392M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
29,
8,
37,
23,
22,
47
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us \n## roberta_chinese_base### Overview\n\nLanguage model: roberta-base\nModel size: 392M\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.### Usage\n\nNOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
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-0.13619887828826904
] |
null | null |
transformers
|
## roberta_chinese_large
### Overview
**Language model:** roberta-large
**Model size:** 1.2G
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
**NOTE:** You have to call **BertTokenizer** instead of RobertaTokenizer !!!
```
import torch
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("clue/roberta_chinese_large")
roberta = BertModel.from_pretrained("clue/roberta_chinese_large")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
{"language": "zh"}
| null |
clue/roberta_chinese_large
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us
|
## roberta_chinese_large
### Overview
Language model: roberta-large
Model size: 1.2G
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
NOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: URL
Website: URL
|
[
"## roberta_chinese_large",
"### Overview\n\nLanguage model: roberta-large\nModel size: 1.2G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us \n",
"## roberta_chinese_large",
"### Overview\n\nLanguage model: roberta-large\nModel size: 1.2G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage\n\nNOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
29,
9,
37,
23,
22,
47
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #zh #endpoints_compatible #region-us \n## roberta_chinese_large### Overview\n\nLanguage model: roberta-large\nModel size: 1.2G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.### Usage\n\nNOTE: You have to call BertTokenizer instead of RobertaTokenizer !!!### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
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] |
null | null |
transformers
|
## xlnet_chinese_large
### Overview
**Language model:** xlnet-large
**Model size:** 1.3G
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
```
import torch
from transformers import XLNetTokenizer,XLNetModel
tokenizer = XLNetTokenizer.from_pretrained("clue/xlnet_chinese_large")
xlnet = XLNetModel.from_pretrained("clue/xlnet_chinese_large")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
{"language": "zh"}
| null |
clue/xlnet_chinese_large
|
[
"transformers",
"pytorch",
"xlnet",
"zh",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #xlnet #zh #endpoints_compatible #region-us
|
## xlnet_chinese_large
### Overview
Language model: xlnet-large
Model size: 1.3G
Language: Chinese
Training data: CLUECorpusSmall
Eval data: CLUE dataset
### Results
For results on downstream tasks like text classification, please refer to this repository.
### Usage
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: URL
Website: URL
|
[
"## xlnet_chinese_large",
"### Overview\n\nLanguage model: xlnet-large\nModel size: 1.3G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
"TAGS\n#transformers #pytorch #xlnet #zh #endpoints_compatible #region-us \n",
"## xlnet_chinese_large",
"### Overview\n\nLanguage model: xlnet-large\nModel size: 1.3G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset",
"### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.",
"### Usage",
"### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
[
26,
10,
38,
23,
4,
47
] |
[
"passage: TAGS\n#transformers #pytorch #xlnet #zh #endpoints_compatible #region-us \n## xlnet_chinese_large### Overview\n\nLanguage model: xlnet-large\nModel size: 1.3G\nLanguage: Chinese\nTraining data: CLUECorpusSmall\nEval data: CLUE dataset### Results\n\nFor results on downstream tasks like text classification, please refer to this repository.### Usage### About CLUE benchmark\n\nOrganization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.\n\nGithub: URL\nWebsite: URL"
] |
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null | null |
transformers
|
DistilCamemBERT-NER
===================
We present DistilCamemBERT-NER, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) based on the [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by two** with the same consumption power thanks to [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base).
Dataset
-------
The dataset used is [wikiner_fr](https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr), which represents ~170k sentences labeled in 5 categories :
* PER: personality ;
* LOC: location ;
* ORG: organization ;
* MISC: miscellaneous entities (movies title, books, etc.) ;
* O: background (Outside entity).
Evaluation results
------------------
| **class** | **precision (%)** | **recall (%)** | **f1 (%)** | **support (#sub-word)** |
| :------------: | :---------------: | :------------: | :--------: | :---------------------: |
| **global** | 98.17 | 98.19 | 98.18 | 378,776 |
| **PER** | 96.78 | 96.87 | 96.82 | 23,754 |
| **LOC** | 94.05 | 93.59 | 93.82 | 27,196 |
| **ORG** | 86.05 | 85.92 | 85.98 | 6,526 |
| **MISC** | 88.78 | 84.69 | 86.69 | 11,891 |
| **O** | 99.26 | 99.47 | 99.37 | 309,409 |
Benchmark
---------
This model performance is compared to 2 reference models (see below) with the metric f1 score. For the mean inference time measure, an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used:
| **model** | **time (ms)** | **PER (%)** | **LOC (%)** | **ORG (%)** | **MISC (%)** | **O (%)** |
| :---------------------------------------------------------------------------------------------------------------: | :-----------: | :---------: | :---------: | :---------: | :-----------: | :-------: |
| [cmarkea/distilcamembert-base-ner](https://huggingface.co/cmarkea/distilcamembert-base-ner) | **43.44** | **96.82** | **93.82** | **85.98** | **86.69** | **99.37** |
| [Davlan/bert-base-multilingual-cased-ner-hrl](https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl) | 87.56 | 79.93 | 72.89 | 61.34 | n/a | 96.04 |
| [flair/ner-french](https://huggingface.co/flair/ner-french) | 314.96 | 82.91 | 76.17 | 70.96 | 76.29 | 97.65 |
How to use DistilCamemBERT-NER
------------------------------
```python
from transformers import pipeline
ner = pipeline(
task='ner',
model="cmarkea/distilcamembert-base-ner",
tokenizer="cmarkea/distilcamembert-base-ner",
aggregation_strategy="simple"
)
result = ner(
"Le Crédit Mutuel Arkéa est une banque Française, elle comprend le CMB "
"qui est une banque située en Bretagne et le CMSO qui est une banque "
"qui se situe principalement en Aquitaine. C'est sous la présidence de "
"Louis Lichou, dans les années 1980 que différentes filiales sont créées "
"au sein du CMB et forment les principales filiales du groupe qui "
"existent encore aujourd'hui (Federal Finance, Suravenir, Financo, etc.)."
)
result
[{'entity_group': 'ORG',
'score': 0.9974479,
'word': 'Crédit Mutuel Arkéa',
'start': 3,
'end': 22},
{'entity_group': 'LOC',
'score': 0.9000358,
'word': 'Française',
'start': 38,
'end': 47},
{'entity_group': 'ORG',
'score': 0.9788757,
'word': 'CMB',
'start': 66,
'end': 69},
{'entity_group': 'LOC',
'score': 0.99919766,
'word': 'Bretagne',
'start': 99,
'end': 107},
{'entity_group': 'ORG',
'score': 0.9594884,
'word': 'CMSO',
'start': 114,
'end': 118},
{'entity_group': 'LOC',
'score': 0.99935514,
'word': 'Aquitaine',
'start': 169,
'end': 178},
{'entity_group': 'PER',
'score': 0.99911094,
'word': 'Louis Lichou',
'start': 208,
'end': 220},
{'entity_group': 'ORG',
'score': 0.96226394,
'word': 'CMB',
'start': 291,
'end': 294},
{'entity_group': 'ORG',
'score': 0.9983959,
'word': 'Federal Finance',
'start': 374,
'end': 389},
{'entity_group': 'ORG',
'score': 0.9984454,
'word': 'Suravenir',
'start': 391,
'end': 400},
{'entity_group': 'ORG',
'score': 0.9985084,
'word': 'Financo',
'start': 402,
'end': 409}]
```
### Optimum + ONNX
```python
from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline
HUB_MODEL = "cmarkea/distilcamembert-base-nli"
tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL)
model = ORTModelForTokenClassification.from_pretrained(HUB_MODEL)
onnx_qa = pipeline("token-classification", model=model, tokenizer=tokenizer)
# Quantized onnx model
quantized_model = ORTModelForTokenClassification.from_pretrained(
HUB_MODEL, file_name="model_quantized.onnx"
)
```
Citation
--------
```bibtex
@inproceedings{delestre:hal-03674695,
TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
URL = {https://hal.archives-ouvertes.fr/hal-03674695},
BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
ADDRESS = {Vannes, France},
YEAR = {2022},
MONTH = Jul,
KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
HAL_ID = {hal-03674695},
HAL_VERSION = {v1},
}
```
|
{"language": "fr", "license": "mit", "datasets": ["Jean-Baptiste/wikiner_fr"], "widget": [{"text": "Boulanger, habitant \u00e0 Boulanger et travaillant dans le magasin Boulanger situ\u00e9 dans la ville de Boulanger. Boulanger a \u00e9crit le livre \u00e9ponyme Boulanger \u00e9dit\u00e9 par la maison d'\u00e9dition Boulanger."}, {"text": "Quentin Jerome Tarantino na\u00eet le 27 mars 1963 \u00e0 Knoxville, dans le Tennessee. Il est le fils de Connie McHugh, une infirmi\u00e8re, n\u00e9e le 3 septembre 1946, et de Tony Tarantino, acteur et musicien amateur n\u00e9 \u00e0 New York. Ce dernier est d'origine italienne par son p\u00e8re ; sa m\u00e8re a des ascendances irlandaises et cherokees. Il est pr\u00e9nomm\u00e9 d'apr\u00e8s Quint Asper, le personnage jou\u00e9 par Burt Reynolds dans la s\u00e9rie Gunsmoke et Quentin Compson, personnage du roman Le Bruit et la Fureur. Son p\u00e8re quitte le domicile familial avant m\u00eame sa naissance. En 1965, sa m\u00e8re d\u00e9m\u00e9nage \u00e0 Torrance, dans la banlieue sud de Los Angeles, et se remarie avec Curtis Zastoupil, un pianiste de bar, qui lui fait d\u00e9couvrir le cin\u00e9ma. Le couple divorce alors que le jeune Quentin a une dizaine d'ann\u00e9es."}]}
|
token-classification
|
cmarkea/distilcamembert-base-ner
|
[
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
DistilCamemBERT-NER
===================
We present DistilCamemBERT-NER, which is DistilCamemBERT fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by Jean-Baptiste/camembert-ner based on the CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by two with the same consumption power thanks to DistilCamemBERT.
Dataset
-------
The dataset used is wikiner\_fr, which represents ~170k sentences labeled in 5 categories :
* PER: personality ;
* LOC: location ;
* ORG: organization ;
* MISC: miscellaneous entities (movies title, books, etc.) ;
* O: background (Outside entity).
Evaluation results
------------------
Benchmark
---------
This model performance is compared to 2 reference models (see below) with the metric f1 score. For the mean inference time measure, an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used:
How to use DistilCamemBERT-NER
------------------------------
### Optimum + ONNX
Citation
--------
|
[
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
77,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Optimum + ONNX\n\n\nCitation\n--------"
] |
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] |
null | null |
transformers
|
DistilCamemBERT-NLI
===================
We present DistilCamemBERT-NLI, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the Natural Language Inference (NLI) task for the french language, also known as recognizing textual entailment (RTE). This model is constructed on the XNLI dataset, which determines whether a premise entails, contradicts or neither entails or contradicts a hypothesis.
This modelization is close to [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) based on [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue especially in the context of cross-encoding like this task. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power, thanks to DistilCamemBERT.
Dataset
-------
The dataset XNLI from [FLUE](https://huggingface.co/datasets/flue) comprises 392,702 premises with their hypothesis for the train and 5,010 couples for the test. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of three labels). Sentence A is called *premise*, and sentence B is called *hypothesis*, then the goal of modelization is determined as follows:
$$P(premise=c\in\{contradiction, entailment, neutral\}\vert hypothesis)$$
Evaluation results
------------------
| **class** | **precision (%)** | **f1-score (%)** | **support** |
| :----------------: | :---------------: | :--------------: | :---------: |
| **global** | 77.70 | 77.45 | 5,010 |
| **contradiction** | 78.00 | 79.54 | 1,670 |
| **entailment** | 82.90 | 78.87 | 1,670 |
| **neutral** | 72.18 | 74.04 | 1,670 |
Benchmark
---------
We compare the [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) model to 2 other modelizations working on the french language. The first one [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) is based on well named [CamemBERT](https://huggingface.co/camembert-base), the french RoBERTa model and the second one [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) based on [mDeBERTav3](https://huggingface.co/microsoft/mdeberta-v3-base) a multilingual model. To compare the performances, the metrics of accuracy and [MCC (Matthews Correlation Coefficient)](https://en.wikipedia.org/wiki/Phi_coefficient) were used. We used an **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** for mean inference time measure.
| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
| :--------------: | :-----------: | :--------------: | :------------: |
| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **51.35** | 77.45 | 66.24 |
| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 105.0 | 81.72 | 72.67 |
| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 299.18 | **83.43** | **75.15** |
Zero-shot classification
------------------------
The main advantage of such modelization is to create a zero-shot classifier allowing text classification without training. This task can be summarized by:
$$P(hypothesis=i\in\mathcal{C}|premise)=\frac{e^{P(premise=entailment\vert hypothesis=i)}}{\sum_{j\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis=j)}}$$
For this part, we use two datasets, the first one: [allocine](https://huggingface.co/datasets/allocine) used to train the sentiment analysis models. The dataset comprises two classes: "positif" and "négatif" appreciation of movie reviews. Here we use "Ce commentaire est {}." as the hypothesis template and "positif" and "négatif" as candidate labels.
| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
| :--------------: | :-----------: | :--------------: | :------------: |
| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **195.54** | 80.59 | 63.71 |
| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39 | **86.37** | **73.74** |
| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 84.97 | 70.05 |
The second one: [mlsum](https://huggingface.co/datasets/mlsum) used to train the summarization models. In this aim, we aggregate sub-topics and select a few of them. We use the articles summary part to predict their topics. In this case, the hypothesis template used is "C'est un article traitant de {}." and the candidate labels are: "économie", "politique", "sport" and "science".
| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
| :--------------: | :-----------: | :--------------: | :------------: |
| [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | **217.77** | **79.30** | **70.55** |
| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 448.27 | 70.7 | 64.10 |
| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 591.34 | 64.45 | 58.67 |
How to use DistilCamemBERT-NLI
------------------------------
```python
from transformers import pipeline
classifier = pipeline(
task='zero-shot-classification',
model="cmarkea/distilcamembert-base-nli",
tokenizer="cmarkea/distilcamembert-base-nli"
)
result = classifier (
sequences="Le style très cinéphile de Quentin Tarantino "
"se reconnaît entre autres par sa narration postmoderne "
"et non linéaire, ses dialogues travaillés souvent "
"émaillés de références à la culture populaire, et ses "
"scènes hautement esthétiques mais d'une violence "
"extrême, inspirées de films d'exploitation, d'arts "
"martiaux ou de western spaghetti.",
candidate_labels="cinéma, technologie, littérature, politique",
hypothesis_template="Ce texte parle de {}."
)
result
{"labels": ["cinéma",
"littérature",
"technologie",
"politique"],
"scores": [0.7164115309715271,
0.12878799438476562,
0.1092301607131958,
0.0455702543258667]}
```
### Optimum + ONNX
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
HUB_MODEL = "cmarkea/distilcamembert-base-nli"
tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL)
model = ORTModelForSequenceClassification.from_pretrained(HUB_MODEL)
onnx_qa = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer)
# Quantized onnx model
quantized_model = ORTModelForSequenceClassification.from_pretrained(
HUB_MODEL, file_name="model_quantized.onnx"
)
```
Citation
--------
```bibtex
@inproceedings{delestre:hal-03674695,
TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
URL = {https://hal.archives-ouvertes.fr/hal-03674695},
BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
ADDRESS = {Vannes, France},
YEAR = {2022},
MONTH = Jul,
KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
HAL_ID = {hal-03674695},
HAL_VERSION = {v1},
}
```
|
{"language": "fr", "license": "mit", "tags": ["zero-shot-classification", "sentence-similarity", "nli"], "datasets": ["flue"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Selon certains physiciens, un univers parall\u00e8le, miroir du n\u00f4tre ou relevant de ce que l'on appelle la th\u00e9orie des branes, autoriserait des neutrons \u00e0 sortir de notre Univers pour y entrer \u00e0 nouveau. L'id\u00e9e a \u00e9t\u00e9 test\u00e9e une nouvelle fois avec le r\u00e9acteur nucl\u00e9aire de l'Institut Laue-Langevin \u00e0 Grenoble, plus pr\u00e9cis\u00e9ment en utilisant le d\u00e9tecteur de l'exp\u00e9rience Stereo initialement con\u00e7u pour chasser des particules de mati\u00e8re noire potentielles, les neutrinos st\u00e9riles.", "candidate_labels": "politique, science, sport, sant\u00e9", "hypothesis_template": "Ce texte parle de {}."}]}
|
zero-shot-classification
|
cmarkea/distilcamembert-base-nli
|
[
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"text-classification",
"zero-shot-classification",
"sentence-similarity",
"nli",
"fr",
"dataset:flue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #zero-shot-classification #sentence-similarity #nli #fr #dataset-flue #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
DistilCamemBERT-NLI
===================
We present DistilCamemBERT-NLI, which is DistilCamemBERT fine-tuned for the Natural Language Inference (NLI) task for the french language, also known as recognizing textual entailment (RTE). This model is constructed on the XNLI dataset, which determines whether a premise entails, contradicts or neither entails or contradicts a hypothesis.
This modelization is close to BaptisteDoyen/camembert-base-xnli based on CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue especially in the context of cross-encoding like this task. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power, thanks to DistilCamemBERT.
Dataset
-------
The dataset XNLI from FLUE comprises 392,702 premises with their hypothesis for the train and 5,010 couples for the test. The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B?) and is a classification task (given two sentences, predict one of three labels). Sentence A is called *premise*, and sentence B is called *hypothesis*, then the goal of modelization is determined as follows:
$$P(premise=c\in{contradiction, entailment, neutral}\vert hypothesis)$$
Evaluation results
------------------
Benchmark
---------
We compare the DistilCamemBERT model to 2 other modelizations working on the french language. The first one BaptisteDoyen/camembert-base-xnli is based on well named CamemBERT, the french RoBERTa model and the second one MoritzLaurer/mDeBERTa-v3-base-mnli-xnli based on mDeBERTav3 a multilingual model. To compare the performances, the metrics of accuracy and MCC (Matthews Correlation Coefficient) were used. We used an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores for mean inference time measure.
Zero-shot classification
------------------------
The main advantage of such modelization is to create a zero-shot classifier allowing text classification without training. This task can be summarized by:
$$P(hypothesis=i\in\mathcal{C}|premise)=\frac{e^{P(premise=entailment\vert hypothesis=i)}}{\sum\_{j\in\mathcal{C}}e^{P(premise=entailment\vert hypothesis=j)}}$$
For this part, we use two datasets, the first one: allocine used to train the sentiment analysis models. The dataset comprises two classes: "positif" and "négatif" appreciation of movie reviews. Here we use "Ce commentaire est {}." as the hypothesis template and "positif" and "négatif" as candidate labels.
The second one: mlsum used to train the summarization models. In this aim, we aggregate sub-topics and select a few of them. We use the articles summary part to predict their topics. In this case, the hypothesis template used is "C'est un article traitant de {}." and the candidate labels are: "économie", "politique", "sport" and "science".
How to use DistilCamemBERT-NLI
------------------------------
### Optimum + ONNX
Citation
--------
|
[
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #zero-shot-classification #sentence-similarity #nli #fr #dataset-flue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
80,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #zero-shot-classification #sentence-similarity #nli #fr #dataset-flue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Optimum + ONNX\n\n\nCitation\n--------"
] |
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] |
null | null |
transformers
|
DistilCamemBERT-QA
==================
We present DistilCamemBERT-QA, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the Question-Answering task for the french language. This model is built using two datasets, FQuAD v1.0 and Piaf, composed of contexts and questions with their answers inside the context.
This modelization is close to [etalab-ia/camembert-base-squadFR-fquad-piaf](https://huggingface.co/etalab-ia/camembert-base-squadFR-fquad-piaf) based on [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue, especially in cross-encoding like this task. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power, thanks to DistilCamemBERT.
Dataset
-------
The dataset comprises FQuAD v1.0 and Piaf with 24'566 questions and answers for the training set and 3'188 for the evaluation set.
Evaluation results and benchmark
--------------------------------
We compare [DistilCamemBERT-QA](https://huggingface.co/cmarkea/distilcamembert-base-qa) to two other modelizations working on the french language. The first one [etalab-ia/camembert-base-squadFR-fquad-piaf](https://huggingface.co/etalab-ia/camembert-base-squadFR-fquad-piaf) is based on well named [CamemBERT](https://huggingface.co/camembert-base), the french RoBERTa model and the second one [fmikaelian/flaubert-base-uncased-squad](https://huggingface.co/fmikaelian/flaubert-base-uncased-squad) is based on [FlauBERT](https://huggingface.co/flaubert/flaubert_base_uncased) another french model based on BERT architecture this time.
For our benchmarks, we do a word-to-word comparison between words that are matching between the predicted answer and the ground truth. We also use f1-score, which measures the intersection quality between predicted responses and ground truth. Finally, we use inclusion score, which measures if the ground truth answer is included in the predicted answer. An **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** was used for the mean inference time measure.
| **model** | **time (ms)** | **exact match (%)** | **f1-score (%)** | **inclusion-score (%)** |
| :--------------: | :-----------: | :--------------: | :------------: | :------------: |
| [cmarkea/distilcamembert-base-qa](https://huggingface.co/cmarkea/distilcamembert-base-qa) | **216.96** | 25.66 | 62.65 | 59.82 |
| [etalab-ia/camembert-base-squadFR-fquad-piaf](https://huggingface.co/etalab-ia/camembert-base-squadFR-fquad-piaf) | 432.17 | **59.76** | **79.57** | **69.23** |
| [fmikaelian/flaubert-base-uncased-squad](https://huggingface.co/fmikaelian/flaubert-base-uncased-squad) | 875.84 | 0.22 | 5.21 | 3.68 |
Do not take into account the results of the FlauBERT model. The modeling seems to be a problem, as the results seem very low.
How to use DistilCamemBERT-QA
------------------------------
```python
from transformers import pipeline
qa_engine = pipeline(
"question-answering",
model="cmarkea/distilcamembert-base-qa",
tokenizer="cmarkea/distilcamembert-base-qa"
)
result = qa_engine(
context="David Fincher, né le 28 août 1962 à Denver (Colorado), "
"est un réalisateur et producteur américain. Il est principalement "
"connu pour avoir réalisé les films Seven, Fight Club, L'Étrange "
"Histoire de Benjamin Button, The Social Network et Gone Girl qui "
"lui ont valu diverses récompenses et nominations aux Oscars du "
"cinéma ou aux Golden Globes. Réputé pour son perfectionnisme, il "
"peut tourner un très grand nombre de prises de ses plans et "
"séquences afin d'obtenir le rendu visuel qu'il désire. Il a "
"également développé et produit les séries télévisées House of "
"Cards (pour laquelle il remporte l'Emmy Award de la meilleure "
"réalisation pour une série dramatique en 2013) et Mindhunter, "
"diffusées sur Netflix.",
question="Quel est le métier de David Fincher ?"
)
result
{'score': 0.7981914281845093,
'start': 61,
'end': 98,
'answer': ' réalisateur et producteur américain.'}
```
### Optimum + ONNX
```python
from optimum.onnxruntime import ORTModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
HUB_MODEL = "cmarkea/distilcamembert-base-qa"
tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL)
model = ORTModelForQuestionAnswering.from_pretrained(HUB_MODEL)
onnx_qa = pipeline("question-answering", model=model, tokenizer=tokenizer)
# Quantized onnx model
quantized_model = ORTModelForQuestionAnswering.from_pretrained(
HUB_MODEL, file_name="model_quantized.onnx"
)
```
Citation
--------
```bibtex
@inproceedings{delestre:hal-03674695,
TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
URL = {https://hal.archives-ouvertes.fr/hal-03674695},
BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
ADDRESS = {Vannes, France},
YEAR = {2022},
MONTH = Jul,
KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
HAL_ID = {hal-03674695},
HAL_VERSION = {v1},
}
```
|
{"language": "fr", "license": "cc-by-nc-sa-3.0", "datasets": ["fquad", "piaf"], "widget": [{"text": "Quand et o\u00f9 est sorti Toy Story ?", "context": "Pixar Animation Studios, ou simplement Pixar dans le langage courant, est une soci\u00e9t\u00e9 am\u00e9ricaine de production de films en images tridimensionnelles de synth\u00e8se. Elle a acquis sa notori\u00e9t\u00e9 gr\u00e2ce \u00e0 Toy Story, premier long m\u00e9trage de ce type, sorti aux \u00c9tats-Unis en 1995. \u00c0 ce jour, le studio d'animation a remport\u00e9 dix-neuf Oscars, quatre Golden Globes et trois Grammy Awards ainsi que de nombreuses autres r\u00e9compenses. Le studio travaille avec PhotoRealistic RenderMan, sa propre version de l'interface de programmation de rendu RenderMan utilis\u00e9e pour cr\u00e9er des images de haute qualit\u00e9. Ses studios de production et son si\u00e8ge social se trouvent au Pixar Campus situ\u00e9 \u00e0 Emeryville pr\u00e8s de San Francisco en Californie."}, {"text": "Quel est le premier long m\u00e9trage du studio ?", "context": "Pixar Animation Studios, ou simplement Pixar dans le langage courant, est une soci\u00e9t\u00e9 am\u00e9ricaine de production de films en images tridimensionnelles de synth\u00e8se. Elle a acquis sa notori\u00e9t\u00e9 gr\u00e2ce \u00e0 Toy Story, premier long m\u00e9trage de ce type, sorti aux \u00c9tats-Unis en 1995. \u00c0 ce jour, le studio d'animation a remport\u00e9 dix-neuf Oscars, quatre Golden Globes et trois Grammy Awards ainsi que de nombreuses autres r\u00e9compenses. Le studio travaille avec PhotoRealistic RenderMan, sa propre version de l'interface de programmation de rendu RenderMan utilis\u00e9e pour cr\u00e9er des images de haute qualit\u00e9. Ses studios de production et son si\u00e8ge social se trouvent au Pixar Campus situ\u00e9 \u00e0 Emeryville pr\u00e8s de San Francisco en Californie."}]}
|
question-answering
|
cmarkea/distilcamembert-base-qa
|
[
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"question-answering",
"fr",
"dataset:fquad",
"dataset:piaf",
"license:cc-by-nc-sa-3.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #question-answering #fr #dataset-fquad #dataset-piaf #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us
|
DistilCamemBERT-QA
==================
We present DistilCamemBERT-QA, which is DistilCamemBERT fine-tuned for the Question-Answering task for the french language. This model is built using two datasets, FQuAD v1.0 and Piaf, composed of contexts and questions with their answers inside the context.
This modelization is close to etalab-ia/camembert-base-squadFR-fquad-piaf based on CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue, especially in cross-encoding like this task. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power, thanks to DistilCamemBERT.
Dataset
-------
The dataset comprises FQuAD v1.0 and Piaf with 24'566 questions and answers for the training set and 3'188 for the evaluation set.
Evaluation results and benchmark
--------------------------------
We compare DistilCamemBERT-QA to two other modelizations working on the french language. The first one etalab-ia/camembert-base-squadFR-fquad-piaf is based on well named CamemBERT, the french RoBERTa model and the second one fmikaelian/flaubert-base-uncased-squad is based on FlauBERT another french model based on BERT architecture this time.
For our benchmarks, we do a word-to-word comparison between words that are matching between the predicted answer and the ground truth. We also use f1-score, which measures the intersection quality between predicted responses and ground truth. Finally, we use inclusion score, which measures if the ground truth answer is included in the predicted answer. An AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used for the mean inference time measure.
Do not take into account the results of the FlauBERT model. The modeling seems to be a problem, as the results seem very low.
How to use DistilCamemBERT-QA
-----------------------------
### Optimum + ONNX
Citation
--------
|
[
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #question-answering #fr #dataset-fquad #dataset-piaf #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us \n",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
70,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #question-answering #fr #dataset-fquad #dataset-piaf #license-cc-by-nc-sa-3.0 #endpoints_compatible #region-us \n### Optimum + ONNX\n\n\nCitation\n--------"
] |
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null | null |
transformers
|
DistilCamemBERT-Sentiment
=========================
We present DistilCamemBERT-Sentiment, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: [Amazon Reviews](https://huggingface.co/datasets/amazon_reviews_multi) and [Allociné.fr](https://huggingface.co/datasets/allocine) to minimize the bias. Indeed, Amazon reviews are similar in messages and relatively shorts, contrary to Allociné critics, who are long and rich texts.
This modelization is close to [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) based on [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by two** with the same consumption power thanks to [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base).
Dataset
-------
The dataset comprises 204,993 reviews for training and 4,999 reviews for the test from Amazon, and 235,516 and 4,729 critics from [Allocine website](https://www.allocine.fr/). The dataset is labeled into five categories:
* 1 star: represents a terrible appreciation,
* 2 stars: bad appreciation,
* 3 stars: neutral appreciation,
* 4 stars: good appreciation,
* 5 stars: excellent appreciation.
Evaluation results
------------------
In addition of accuracy (called here *exact accuracy*) in order to be robust to +/-1 star estimation errors, we will take the following definition as a performance measure:
$$\mathrm{top\!-\!2\; acc}=\frac{1}{|\mathcal{O}|}\sum_{i\in\mathcal{O}}\sum_{0\leq l < 2}\mathbb{1}(\hat{f}_{i,l}=y_i)$$
where \\(\hat{f}_l\\) is the l-th largest predicted label, \\(y\\) the true label, \\(\mathcal{O}\\) is the test set of the observations and \\(\mathbb{1}\\) is the indicator function.
| **class** | **exact accuracy (%)** | **top-2 acc (%)** | **support** |
| :---------: | :--------------------: | :---------------: | :---------: |
| **global** | 61.01 | 88.80 | 9,698 |
| **1 star** | 87.21 | 77.17 | 1,905 |
| **2 stars** | 79.19 | 84.75 | 1,935 |
| **3 stars** | 77.85 | 78.98 | 1,974 |
| **4 stars** | 78.61 | 90.22 | 1,952 |
| **5 stars** | 85.96 | 82.92 | 1,932 |
Benchmark
---------
This model is compared to 3 reference models (see below). As each model doesn't have the exact definition of targets, we detail the performance measure used for each. An **AMD Ryzen 5 4500U @ 2.3GHz with 6 cores** was used for the mean inference time measure.
#### bert-base-multilingual-uncased-sentiment
[nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same.
| **model** | **time (ms)** | **exact accuracy (%)** | **top-2 acc (%)** |
| :-------: | :-----------: | :--------------------: | :---------------: |
| [cmarkea/distilcamembert-base-sentiment](https://huggingface.co/cmarkea/distilcamembert-base-sentiment) | **95.56** | **61.01** | **88.80** |
| [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) | 187.70 | 54.41 | 82.82 |
#### tf-allociné and barthez-sentiment-classification
[tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) based on [CamemBERT](https://huggingface.co/camembert-base) model and [moussaKam/barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentiment-classification) based on [BARThez](https://huggingface.co/moussaKam/barthez) use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the *"1 star"* and *"2 stars"* labels for the *negative* sentiments and *"4 stars"* and *"5 stars"* for *positive* sentiments. We exclude the *"3 stars"* which can be interpreted as a *neutral* class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition.
| **model** | **time (ms)** | **exact accuracy (%)** |
| :-------: | :-----------: | :--------------------: |
| [cmarkea/distilcamembert-base-sentiment](https://huggingface.co/cmarkea/distilcamembert-base-sentiment) | **95.56** | **97.52** |
| [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) | 329.74 | 95.69 |
| [moussaKam/barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentiment-classification) | 197.95 | 94.29 |
How to use DistilCamemBERT-Sentiment
------------------------------------
```python
from transformers import pipeline
analyzer = pipeline(
task='text-classification',
model="cmarkea/distilcamembert-base-sentiment",
tokenizer="cmarkea/distilcamembert-base-sentiment"
)
result = analyzer(
"J'aime me promener en forêt même si ça me donne mal aux pieds.",
return_all_scores=True
)
result
[{'label': '1 star',
'score': 0.047529436647892},
{'label': '2 stars',
'score': 0.14150355756282806},
{'label': '3 stars',
'score': 0.3586442470550537},
{'label': '4 stars',
'score': 0.3181498646736145},
{'label': '5 stars',
'score': 0.13417290151119232}]
```
### Optimum + ONNX
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
HUB_MODEL = "cmarkea/distilcamembert-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL)
model = ORTModelForSequenceClassification.from_pretrained(HUB_MODEL)
onnx_qa = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Quantized onnx model
quantized_model = ORTModelForSequenceClassification.from_pretrained(
HUB_MODEL, file_name="model_quantized.onnx"
)
```
Citation
--------
```bibtex
@inproceedings{delestre:hal-03674695,
TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
URL = {https://hal.archives-ouvertes.fr/hal-03674695},
BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
ADDRESS = {Vannes, France},
YEAR = {2022},
MONTH = Jul,
KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
HAL_ID = {hal-03674695},
HAL_VERSION = {v1},
}
```
|
{"language": "fr", "license": "mit", "datasets": ["amazon_reviews_multi", "allocine"], "widget": [{"text": "Je pensais lire un livre nul, mais finalement je l'ai trouv\u00e9 super !"}, {"text": "Cette banque est tr\u00e8s bien, mais elle n'offre pas les services de paiements sans contact."}, {"text": "Cette banque est tr\u00e8s bien et elle offre en plus les services de paiements sans contact."}]}
|
text-classification
|
cmarkea/distilcamembert-base-sentiment
|
[
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"camembert",
"text-classification",
"fr",
"dataset:amazon_reviews_multi",
"dataset:allocine",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #fr #dataset-amazon_reviews_multi #dataset-allocine #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
DistilCamemBERT-Sentiment
=========================
We present DistilCamemBERT-Sentiment, which is DistilCamemBERT fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: Amazon Reviews and Allociné.fr to minimize the bias. Indeed, Amazon reviews are similar in messages and relatively shorts, contrary to Allociné critics, who are long and rich texts.
This modelization is close to tblard/tf-allocine based on CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by two with the same consumption power thanks to DistilCamemBERT.
Dataset
-------
The dataset comprises 204,993 reviews for training and 4,999 reviews for the test from Amazon, and 235,516 and 4,729 critics from Allocine website. The dataset is labeled into five categories:
* 1 star: represents a terrible appreciation,
* 2 stars: bad appreciation,
* 3 stars: neutral appreciation,
* 4 stars: good appreciation,
* 5 stars: excellent appreciation.
Evaluation results
------------------
In addition of accuracy (called here *exact accuracy*) in order to be robust to +/-1 star estimation errors, we will take the following definition as a performance measure:
$$\mathrm{top!-!2; acc}=\frac{1}{|\mathcal{O}|}\sum\_{i\in\mathcal{O}}\sum\_{0\leq l < 2}\mathbb{1}(\hat{f}\_{i,l}=y\_i)$$
where \(\hat{f}\_l\) is the l-th largest predicted label, \(y\) the true label, \(\mathcal{O}\) is the test set of the observations and \(\mathbb{1}\) is the indicator function.
Benchmark
---------
This model is compared to 3 reference models (see below). As each model doesn't have the exact definition of targets, we detail the performance measure used for each. An AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used for the mean inference time measure.
#### bert-base-multilingual-uncased-sentiment
nlptown/bert-base-multilingual-uncased-sentiment is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same.
#### tf-allociné and barthez-sentiment-classification
tblard/tf-allocine based on CamemBERT model and moussaKam/barthez-sentiment-classification based on BARThez use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the *"1 star"* and *"2 stars"* labels for the *negative* sentiments and *"4 stars"* and *"5 stars"* for *positive* sentiments. We exclude the *"3 stars"* which can be interpreted as a *neutral* class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition.
How to use DistilCamemBERT-Sentiment
------------------------------------
### Optimum + ONNX
Citation
--------
|
[
"#### bert-base-multilingual-uncased-sentiment\n\n\nnlptown/bert-base-multilingual-uncased-sentiment is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same.",
"#### tf-allociné and barthez-sentiment-classification\n\n\ntblard/tf-allocine based on CamemBERT model and moussaKam/barthez-sentiment-classification based on BARThez use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the *\"1 star\"* and *\"2 stars\"* labels for the *negative* sentiments and *\"4 stars\"* and *\"5 stars\"* for *positive* sentiments. We exclude the *\"3 stars\"* which can be interpreted as a *neutral* class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition.\n\n\n\nHow to use DistilCamemBERT-Sentiment\n------------------------------------",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
"TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #fr #dataset-amazon_reviews_multi #dataset-allocine #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### bert-base-multilingual-uncased-sentiment\n\n\nnlptown/bert-base-multilingual-uncased-sentiment is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same.",
"#### tf-allociné and barthez-sentiment-classification\n\n\ntblard/tf-allocine based on CamemBERT model and moussaKam/barthez-sentiment-classification based on BARThez use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the *\"1 star\"* and *\"2 stars\"* labels for the *negative* sentiments and *\"4 stars\"* and *\"5 stars\"* for *positive* sentiments. We exclude the *\"3 stars\"* which can be interpreted as a *neutral* class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition.\n\n\n\nHow to use DistilCamemBERT-Sentiment\n------------------------------------",
"### Optimum + ONNX\n\n\nCitation\n--------"
] |
[
78,
82,
194,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #onnx #safetensors #camembert #text-classification #fr #dataset-amazon_reviews_multi #dataset-allocine #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### bert-base-multilingual-uncased-sentiment\n\n\nnlptown/bert-base-multilingual-uncased-sentiment is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same.#### tf-allociné and barthez-sentiment-classification\n\n\ntblard/tf-allocine based on CamemBERT model and moussaKam/barthez-sentiment-classification based on BARThez use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the *\"1 star\"* and *\"2 stars\"* labels for the *negative* sentiments and *\"4 stars\"* and *\"5 stars\"* for *positive* sentiments. We exclude the *\"3 stars\"* which can be interpreted as a *neutral* class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition.\n\n\n\nHow to use DistilCamemBERT-Sentiment\n------------------------------------### Optimum + ONNX\n\n\nCitation\n--------"
] |
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null | null |
transformers
|
DistilCamemBERT
===============
We present a distillation version of the well named [CamemBERT](https://huggingface.co/camembert-base), a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is to drastically reduce the complexity of the model while preserving the performances. The proof of concept is shown in the [DistilBERT paper](https://arxiv.org/abs/1910.01108) and the code used for the training is inspired by the code of [DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation).
Loss function
-------------
The training for the distilled model (student model) is designed to be the closest as possible to the original model (teacher model). To perform this the loss function is composed of 3 parts:
* DistilLoss: a distillation loss which measures the silimarity between the probabilities at the outputs of the student and teacher models with a cross-entropy loss on the MLM task ;
* CosineLoss: a cosine embedding loss. This loss function is applied on the last hidden layers of student and teacher models to guarantee a collinearity between them ;
* MLMLoss: and finaly a Masked Language Modeling (MLM) task loss to perform the student model with the original task of the teacher model.
The final loss function is a combination of these three losses functions. We use the following ponderation:
$$Loss = 0.5 \times DistilLoss + 0.3 \times CosineLoss + 0.2 \times MLMLoss$$
Dataset
-------
To limit the bias between the student and teacher models, the dataset used for the DstilCamemBERT training is the same as the camembert-base training one: OSCAR. The French part of this dataset approximately represents 140 GB on a hard drive disk.
Training
--------
We pre-trained the model on a nVidia Titan RTX during 18 days.
Evaluation results
------------------
| Dataset name | f1-score |
| :----------: | :------: |
| [FLUE](https://huggingface.co/datasets/flue) CLS | 83% |
| [FLUE](https://huggingface.co/datasets/flue) PAWS-X | 77% |
| [FLUE](https://huggingface.co/datasets/flue) XNLI | 77% |
| [wikiner_fr](https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr) NER | 98% |
How to use DistilCamemBERT
--------------------------
Load DistilCamemBERT and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cmarkea/distilcamembert-base")
model = AutoModel.from_pretrained("cmarkea/distilcamembert-base")
model.eval()
...
```
Filling masks using pipeline :
```python
from transformers import pipeline
model_fill_mask = pipeline("fill-mask", model="cmarkea/distilcamembert-base", tokenizer="cmarkea/distilcamembert-base")
results = model_fill_mask("Le camembert est <mask> :)")
results
[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.3878222405910492, 'token': 7200},
{'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.06469205021858215, 'token': 2183},
{'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.04534877464175224, 'token': 1654},
{'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.04128391295671463, 'token': 26202},
{'sequence': '<s> Le camembert est magnifique :)</s>', 'score': 0.02425697259604931, 'token': 1509}]
```
Citation
--------
```bibtex
@inproceedings{delestre:hal-03674695,
TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
URL = {https://hal.archives-ouvertes.fr/hal-03674695},
BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
ADDRESS = {Vannes, France},
YEAR = {2022},
MONTH = Jul,
KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
HAL_ID = {hal-03674695},
HAL_VERSION = {v1},
}
```
|
{"language": "fr", "license": "mit", "datasets": ["oscar"], "widget": [{"text": "J'aime lire les <mask> de SF."}]}
|
fill-mask
|
cmarkea/distilcamembert-base
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1910.01108",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.01108"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #tf #safetensors #camembert #fill-mask #fr #dataset-oscar #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
DistilCamemBERT
===============
We present a distillation version of the well named CamemBERT, a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is to drastically reduce the complexity of the model while preserving the performances. The proof of concept is shown in the DistilBERT paper and the code used for the training is inspired by the code of DistilBERT.
Loss function
-------------
The training for the distilled model (student model) is designed to be the closest as possible to the original model (teacher model). To perform this the loss function is composed of 3 parts:
* DistilLoss: a distillation loss which measures the silimarity between the probabilities at the outputs of the student and teacher models with a cross-entropy loss on the MLM task ;
* CosineLoss: a cosine embedding loss. This loss function is applied on the last hidden layers of student and teacher models to guarantee a collinearity between them ;
* MLMLoss: and finaly a Masked Language Modeling (MLM) task loss to perform the student model with the original task of the teacher model.
The final loss function is a combination of these three losses functions. We use the following ponderation:
$$Loss = 0.5 \times DistilLoss + 0.3 \times CosineLoss + 0.2 \times MLMLoss$$
Dataset
-------
To limit the bias between the student and teacher models, the dataset used for the DstilCamemBERT training is the same as the camembert-base training one: OSCAR. The French part of this dataset approximately represents 140 GB on a hard drive disk.
Training
--------
We pre-trained the model on a nVidia Titan RTX during 18 days.
Evaluation results
------------------
How to use DistilCamemBERT
--------------------------
Load DistilCamemBERT and its sub-word tokenizer :
Filling masks using pipeline :
Citation
--------
|
[] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #camembert #fill-mask #fr #dataset-oscar #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
67
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #camembert #fill-mask #fr #dataset-oscar #arxiv-1910.01108 #license-mit #autotrain_compatible #endpoints_compatible #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. -->
# 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: 0.8651
- Matthews Correlation: 0.5475
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5233 | 1.0 | 535 | 0.5353 | 0.4004 |
| 0.3497 | 2.0 | 1070 | 0.5165 | 0.5076 |
| 0.2386 | 3.0 | 1605 | 0.6661 | 0.5161 |
| 0.1745 | 4.0 | 2140 | 0.7730 | 0.5406 |
| 0.1268 | 5.0 | 2675 | 0.8651 | 0.5475 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.6
|
{"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": 0.5474713423103301, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
cnu/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"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 #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: 0.8651
* Matthews Correlation: 0.5475
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 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.6
|
[
"### 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 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.6"
] |
[
"TAGS\n#transformers #pytorch #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 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.6"
] |
[
63,
98,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #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 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.6"
] |
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null | null |
transformers
|
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-cail-minlm")
model = AutoModel.from_pretrained("coastalcph/fairlex-cail-minlm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |
|
{"language": "zh", "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "\u4e0a\u8ff0\u4e8b\u5b9e\uff0c\u88ab\u544a\u4eba\u5728\u5ead\u5ba1\u8fc7\u7a0b\u4e2d\u4ea6\u65e0\u5f02\u8bae\uff0c\u4e14\u6709<mask>\u7684\u9648\u8ff0\uff0c\u73b0\u573a\u8fa8\u8ba4\u7b14\u5f55\u53ca\u7167\u7247\uff0c\u88ab\u544a\u4eba\u7684\u524d\u79d1\u5211\u4e8b\u5224\u51b3\u4e66\uff0c\u91ca\u653e\u8bc1\u660e\u6750\u6599\uff0c\u6293\u83b7\u7ecf\u8fc7\uff0c\u88ab\u544a\u4eba\u7684\u4f9b\u8ff0\u53ca\u8eab\u4efd\u8bc1\u660e\u7b49\u8bc1\u636e\u8bc1\u5b9e\uff0c\u8db3\u4ee5\u8ba4\u5b9a\u3002"}]}
|
fill-mask
|
coastalcph/fairlex-cail-minilm
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"legal",
"fairlex",
"zh",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #zh #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
Pre-training details
--------------------
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
Models list
-----------
Model name: 'coastalcph/fairlex-ecthr-minlm', Training corpora: ECtHR, Language: 'en'
Model name: 'coastalcph/fairlex-scotus-minlm', Training corpora: SCOTUS, Language: 'en'
Model name: 'coastalcph/fairlex-fscs-minlm', Training corpora: FSCS, Language: ['de', 'fr', 'it']
Model name: 'coastalcph/fairlex-cail-minlm', Training corpora: CAIL, Language: 'zh'
Load Pretrained Model
---------------------
Evaluation on downstream tasks
------------------------------
Consider the experiments in the article:
*Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
Author - Publication
--------------------
Ilias Chalkidis on behalf of CoAStaL NLP Group
| Github: @ilias.chalkidis | Twitter: @KiddoThe2B |
|
[] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #zh #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
60
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #zh #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-ecthr-minilm")
model = AutoModel.from_pretrained("coastalcph/fairlex-ecthr-minilm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |
|
{"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "The applicant submitted that her husband was subjected to treatment amounting to <mask> whilst in the custody of Adana Security Directorate"}]}
|
fill-mask
|
coastalcph/fairlex-ecthr-minilm
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"legal",
"fairlex",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
Pre-training details
--------------------
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
Models list
-----------
Model name: 'coastalcph/fairlex-ecthr-minlm', Training corpora: ECtHR, Language: 'en'
Model name: 'coastalcph/fairlex-scotus-minlm', Training corpora: SCOTUS, Language: 'en'
Model name: 'coastalcph/fairlex-fscs-minlm', Training corpora: FSCS, Language: ['de', 'fr', 'it']
Model name: 'coastalcph/fairlex-cail-minlm', Training corpora: CAIL, Language: 'zh'
Load Pretrained Model
---------------------
Evaluation on downstream tasks
------------------------------
Consider the experiments in the article:
*Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
Author - Publication
--------------------
Ilias Chalkidis on behalf of CoAStaL NLP Group
| Github: @ilias.chalkidis | Twitter: @KiddoThe2B |
|
[] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-fscs-minlm")
model = AutoModel.from_pretrained("coastalcph/fairlex-fscs-minlm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |
|
{"language": ["de", "fr", "it"], "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "Aus seinem damaligen strafbaren Verhalten resultierte eine Forderung der Nachlassverwaltung eines <mask>, wor\u00fcber eine aussergerichtliche Vereinbarung \u00fcber Fr. 500'000."}, {"text": " Elle avait pour but social les <mask> dans le domaine des changes, en particulier l'exploitation d'une plateforme internet."}, {"text": "Il Pretore ha accolto la petizione con sentenza 16 luglio 2015, accordando all'attore l'importo <mask>, con interessi di mora a partire dalla notifica del precetto esecutivo, e ha rigettato in tale misura l'opposizione interposta a quest'ultimo."}]}
|
fill-mask
|
coastalcph/fairlex-fscs-minilm
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"legal",
"fairlex",
"de",
"fr",
"it",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de",
"fr",
"it"
] |
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #de #fr #it #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
Pre-training details
--------------------
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
Models list
-----------
Model name: 'coastalcph/fairlex-ecthr-minlm', Training corpora: ECtHR, Language: 'en'
Model name: 'coastalcph/fairlex-scotus-minlm', Training corpora: SCOTUS, Language: 'en'
Model name: 'coastalcph/fairlex-fscs-minlm', Training corpora: FSCS, Language: ['de', 'fr', 'it']
Model name: 'coastalcph/fairlex-cail-minlm', Training corpora: CAIL, Language: 'zh'
Load Pretrained Model
---------------------
Evaluation on downstream tasks
------------------------------
Consider the experiments in the article:
*Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
Author - Publication
--------------------
Ilias Chalkidis on behalf of CoAStaL NLP Group
| Github: @ilias.chalkidis | Twitter: @KiddoThe2B |
|
[] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #de #fr #it #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
64
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #legal #fairlex #de #fr #it #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-scotus-minlm")
model = AutoModel.from_pretrained("coastalcph/fairlex-scotus-minlm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |
|
{"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["legal", "fairlex"], "pipeline_tag": "fill-mask", "widget": [{"text": "Because the Court granted <mask> before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants\u2019 appeals."}]}
|
fill-mask
|
coastalcph/fairlex-scotus-minilm
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"legal",
"fairlex",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
FairLex: A multilingual benchmark for evaluating fairness in legal text processing
==================================================================================
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
Pre-training details
--------------------
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
Models list
-----------
Model name: 'coastalcph/fairlex-ecthr-minlm', Training corpora: ECtHR, Language: 'en'
Model name: 'coastalcph/fairlex-scotus-minlm', Training corpora: SCOTUS, Language: 'en'
Model name: 'coastalcph/fairlex-fscs-minlm', Training corpora: FSCS, Language: ['de', 'fr', 'it']
Model name: 'coastalcph/fairlex-cail-minlm', Training corpora: CAIL, Language: 'zh'
Load Pretrained Model
---------------------
Evaluation on downstream tasks
------------------------------
Consider the experiments in the article:
*Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
Author - Publication
--------------------
Ilias Chalkidis on behalf of CoAStaL NLP Group
| Github: @ilias.chalkidis | Twitter: @KiddoThe2B |
|
[] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #legal #fairlex #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Kohaku DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
cocoaclef/DialoGPT-small-kohaku
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kohaku DialoGPT Model
|
[
"# Kohaku DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kohaku DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Kohaku DialoGPT Model"
] |
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null | null |
transformers
|
# Rick Morty DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
codealtgeek/DiabloGPT-medium-rickmorty
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Morty DialoGPT Model
|
[
"# Rick Morty DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Morty DialoGPT Model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Morty DialoGPT Model"
] |
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] |
null | null |
transformers
|
HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and Nigerian English Speeches. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.T
Fine-tuned facebook/wav2vec2-large on English using the UISpeech Corpus. When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: https://github.com/amceejay/HIYACCENT-NE-Speech-Recognition-System
##Usage: The model can be used directly (without a language model) as follows...
#Using the ASRecognition library:
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="codeceejay/HIYACCENT_Wav2Vec2")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
##Writing your own inference speech:
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "codeceejay/HIYACCENT_Wav2Vec2"
SAMPLES = 10
#You can use common_voice/timit or Nigerian Accented Speeches can also be found here: https://openslr.org/70/
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
|
{}
|
automatic-speech-recognition
|
codeceejay/HIYACCENT_Wav2Vec2
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and Nigerian English Speeches. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.T
Fine-tuned facebook/wav2vec2-large on English using the UISpeech Corpus. When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: URL
##Usage: The model can be used directly (without a language model) as follows...
#Using the ASRecognition library:
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="codeceejay/HIYACCENT_Wav2Vec2")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
##Writing your own inference speech:
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "codeceejay/HIYACCENT_Wav2Vec2"
SAMPLES = 10
#You can use common_voice/timit or Nigerian Accented Speeches can also be found here: URL
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = URL(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = URL(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
|
[
"# Preprocessing the datasets.",
"# We need to read the audio files as arrays\ndef speech_file_to_array_fn(batch):\n speech_array, sampling_rate = URL(batch[\"path\"], sr=16_000)\n batch[\"speech\"] = speech_array\n batch[\"sentence\"] = batch[\"sentence\"].upper()\n return batch\n\ntest_dataset = test_dataset.map(speech_file_to_array_fn)\ninputs = processor(test_dataset[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n\nwith torch.no_grad():\n logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits\n\npredicted_ids = URL(logits, dim=-1)\npredicted_sentences = processor.batch_decode(predicted_ids)\n\nfor i, predicted_sentence in enumerate(predicted_sentences):\n print(\"-\" * 100)\n print(\"Reference:\", test_dataset[i][\"sentence\"])\n print(\"Prediction:\", predicted_sentence)"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# Preprocessing the datasets.",
"# We need to read the audio files as arrays\ndef speech_file_to_array_fn(batch):\n speech_array, sampling_rate = URL(batch[\"path\"], sr=16_000)\n batch[\"speech\"] = speech_array\n batch[\"sentence\"] = batch[\"sentence\"].upper()\n return batch\n\ntest_dataset = test_dataset.map(speech_file_to_array_fn)\ninputs = processor(test_dataset[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n\nwith torch.no_grad():\n logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits\n\npredicted_ids = URL(logits, dim=-1)\npredicted_sentences = processor.batch_decode(predicted_ids)\n\nfor i, predicted_sentence in enumerate(predicted_sentences):\n print(\"-\" * 100)\n print(\"Reference:\", test_dataset[i][\"sentence\"])\n print(\"Prediction:\", predicted_sentence)"
] |
[
41,
9,
295
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n# Preprocessing the datasets.# We need to read the audio files as arrays\ndef speech_file_to_array_fn(batch):\n speech_array, sampling_rate = URL(batch[\"path\"], sr=16_000)\n batch[\"speech\"] = speech_array\n batch[\"sentence\"] = batch[\"sentence\"].upper()\n return batch\n\ntest_dataset = test_dataset.map(speech_file_to_array_fn)\ninputs = processor(test_dataset[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n\nwith torch.no_grad():\n logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits\n\npredicted_ids = URL(logits, dim=-1)\npredicted_sentences = processor.batch_decode(predicted_ids)\n\nfor i, predicted_sentence in enumerate(predicted_sentences):\n print(\"-\" * 100)\n print(\"Reference:\", test_dataset[i][\"sentence\"])\n print(\"Prediction:\", predicted_sentence)"
] |
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null | null |
transformers
|
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version and `base-uncased` (the one you're looking at) as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/).
For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert)
## Pre-trained models
| Model | Arch. | Training data |
| ----------------------------------- | -------------- | ---------------------- |
| `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) |
| `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) |
## How to use Calbert with HuggingFace
#### Load Calbert and its tokenizer:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-base-uncased")
model = AutoModel.from_pretrained("codegram/calbert-base-uncased")
model.eval() # disable dropout (or leave in train mode to finetune
```
#### Filling masks using pipeline
```python
from transformers import pipeline
calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-base-uncased", tokenizer="codegram/calbert-base-uncased")
results = calbert_fill_mask("M'agrada [MASK] això")
# results
# [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.614592969417572, 'token': 61},
# {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.06058056280016899, 'token': 4867},
# {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.017195818945765495, 'token': 43},
# {'sequence': "[CLS] m'agrada llegir aixo[SEP]", 'score': 0.016321714967489243, 'token': 684},
# {'sequence': "[CLS] m'agrada escriure aixo[SEP]", 'score': 0.012185849249362946, 'token': 1306}]
```
#### Extract contextual embedding features from Calbert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("M'és una mica igual")
# ['▁m', "'", 'es', '▁una', '▁mica', '▁igual']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [2, 109, 7, 71, 36, 371, 1103, 3]
# NB: Can be done in one step : tokenize.encode("M'és una mica igual")
# Feed tokens to Calbert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = model(encoded_sentence)
embeddings.size()
# torch.Size([1, 8, 768])
embeddings.detach()
# tensor([[[-0.0261, 0.1166, -0.1075, ..., -0.0368, 0.0193, 0.0017],
# [ 0.1289, -0.2252, 0.9881, ..., -0.1353, 0.3534, 0.0734],
# [-0.0328, -1.2364, 0.9466, ..., 0.3455, 0.7010, -0.2085],
# ...,
# [ 0.0397, -1.0228, -0.2239, ..., 0.2932, 0.1248, 0.0813],
# [-0.0261, 0.1165, -0.1074, ..., -0.0368, 0.0193, 0.0017],
# [-0.1934, -0.2357, -0.2554, ..., 0.1831, 0.6085, 0.1421]]])
```
## Authors
CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research.
<a href="https://huggingface.co/exbert/?model=codegram/calbert-base-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "ca", "license": "mit", "tags": ["masked-lm", "catalan", "exbert"]}
| null |
codegram/calbert-base-uncased
|
[
"transformers",
"pytorch",
"albert",
"masked-lm",
"catalan",
"exbert",
"ca",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ca"
] |
TAGS
#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us
|
Calbert: a Catalan Language Model
=================================
Introduction
------------
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its 'tiny-uncased' version and 'base-uncased' (the one you're looking at) as well, and was pretrained on the OSCAR dataset.
For further information or requests, please go to the GitHub repository
Pre-trained models
------------------
Model: 'codegram' / 'calbert-tiny-uncased', Arch.: Tiny (uncased), Training data: OSCAR (4.3 GB of text)
Model: 'codegram' / 'calbert-base-uncased', Arch.: Base (uncased), Training data: OSCAR (4.3 GB of text)
How to use Calbert with HuggingFace
-----------------------------------
#### Load Calbert and its tokenizer:
#### Filling masks using pipeline
#### Extract contextual embedding features from Calbert output
Authors
-------
CALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.
[<img width="300px" src="URL
</a>](URL%20força%20saber-ne%20més)
|
[
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%20saber-ne%20més)"
] |
[
"TAGS\n#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us \n",
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%20saber-ne%20més)"
] |
[
42,
12,
9,
75
] |
[
"passage: TAGS\n#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us \n#### Load Calbert and its tokenizer:#### Filling masks using pipeline#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%20saber-ne%20més)"
] |
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] |
null | null |
transformers
|
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at) and `base-uncased` as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/).
For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert)
## Pre-trained models
| Model | Arch. | Training data |
| ----------------------------------- | -------------- | ---------------------- |
| `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) |
| `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) |
## How to use Calbert with HuggingFace
#### Load Calbert and its tokenizer:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-tiny-uncased")
model = AutoModel.from_pretrained("codegram/calbert-tiny-uncased")
model.eval() # disable dropout (or leave in train mode to finetune
```
#### Filling masks using pipeline
```python
from transformers import pipeline
calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-tiny-uncased", tokenizer="codegram/calbert-tiny-uncased")
results = calbert_fill_mask("M'agrada [MASK] això")
# results
# [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.4403671622276306, 'token': 61},
# {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.050061386078596115, 'token': 43},
# {'sequence': "[CLS] m'agrada veure aixo[SEP]", 'score': 0.026286985725164413, 'token': 157},
# {'sequence': "[CLS] m'agrada bastant aixo[SEP]", 'score': 0.022483550012111664, 'token': 2143},
# {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.014491282403469086, 'token': 4867}]
```
#### Extract contextual embedding features from Calbert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("M'és una mica igual")
# ['▁m', "'", 'es', '▁una', '▁mica', '▁igual']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [2, 109, 7, 71, 36, 371, 1103, 3]
# NB: Can be done in one step : tokenize.encode("M'és una mica igual")
# Feed tokens to Calbert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = model(encoded_sentence)
embeddings.size()
# torch.Size([1, 8, 312])
embeddings.detach()
# tensor([[[-0.2726, -0.9855, 0.9643, ..., 0.3511, 0.3499, -0.1984],
# [-0.2824, -1.1693, -0.2365, ..., -3.1866, -0.9386, -1.3718],
# [-2.3645, -2.2477, -1.6985, ..., -1.4606, -2.7294, 0.2495],
# ...,
# [ 0.8800, -0.0244, -3.0446, ..., 0.5148, -3.0903, 1.1879],
# [ 1.1300, 0.2425, 0.2162, ..., -0.5722, -2.2004, 0.4045],
# [ 0.4549, -0.2378, -0.2290, ..., -2.1247, -2.2769, -0.0820]]])
```
## Authors
CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research.
<a href="https://huggingface.co/exbert/?model=codegram/calbert-tiny-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "ca", "license": "mit", "tags": ["masked-lm", "catalan", "exbert"]}
| null |
codegram/calbert-tiny-uncased
|
[
"transformers",
"pytorch",
"albert",
"masked-lm",
"catalan",
"exbert",
"ca",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ca"
] |
TAGS
#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us
|
Calbert: a Catalan Language Model
=================================
Introduction
------------
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its 'tiny-uncased' version (the one you're looking at) and 'base-uncased' as well, and was pretrained on the OSCAR dataset.
For further information or requests, please go to the GitHub repository
Pre-trained models
------------------
Model: 'codegram' / 'calbert-tiny-uncased', Arch.: Tiny (uncased), Training data: OSCAR (4.3 GB of text)
Model: 'codegram' / 'calbert-base-uncased', Arch.: Base (uncased), Training data: OSCAR (4.3 GB of text)
How to use Calbert with HuggingFace
-----------------------------------
#### Load Calbert and its tokenizer:
#### Filling masks using pipeline
#### Extract contextual embedding features from Calbert output
Authors
-------
CALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.
[<img width="300px" src="URL
</a>](URL%20força%20saber-ne%20més)
|
[
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%20saber-ne%20més)"
] |
[
"TAGS\n#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us \n",
"#### Load Calbert and its tokenizer:",
"#### Filling masks using pipeline",
"#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%20saber-ne%20més)"
] |
[
42,
12,
9,
75
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[
"passage: TAGS\n#transformers #pytorch #albert #masked-lm #catalan #exbert #ca #license-mit #endpoints_compatible #region-us \n#### Load Calbert and its tokenizer:#### Filling masks using pipeline#### Extract contextual embedding features from Calbert output\n\n\nAuthors\n-------\n\n\nCALBERT was trained and evaluated by Txus Bach, as part of Codegram's applied research.\n\n\n[<img width=\"300px\" src=\"URL\n</a>](URL%20força%20saber-ne%20més)"
] |
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] |
null | null |
transformers
|
This model is a paraphraser designed for the Adversarial Paraphrasing Task described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Please refer to `nap_generation.py` on the github repository for ways to better utilize this model using concepts of top-k sampling and top-p sampling. The demo on huggingface will output only one sentence which will most likely be the same as the input sentence since the model is supposed to output using beam search and sampling.
Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git
Please cite the following if you use this model:
```bib
@inproceedings{nighojkar-licato-2021-improving,
title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
author = "Nighojkar, Animesh and
Licato, John",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.552",
pages = "7106--7116",
abstract = "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.",
}
```
|
{}
|
text2text-generation
|
AMHR/T5-for-Adversarial-Paraphrasing
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model is a paraphraser designed for the Adversarial Paraphrasing Task described and used in this paper: URL
Please refer to 'nap_generation.py' on the github repository for ways to better utilize this model using concepts of top-k sampling and top-p sampling. The demo on huggingface will output only one sentence which will most likely be the same as the input sentence since the model is supposed to output using beam search and sampling.
Github repository: URL
Please cite the following if you use this model:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
53
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git
Please cite the following if you use this model:
```bib
@inproceedings{nighojkar-licato-2021-improving,
title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
author = "Nighojkar, Animesh and
Licato, John",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.552",
pages = "7106--7116",
abstract = "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.",
}
```
|
{}
|
text-classification
|
AMHR/adversarial-paraphrasing-detector
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: URL
Github repository: URL
Please cite the following if you use this model:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
42
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #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. -->
# 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: 0.0611
- Precision: 0.9272
- Recall: 0.9382
- F1: 0.9327
- Accuracy: 0.9843
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2432 | 1.0 | 878 | 0.0689 | 0.9132 | 0.9203 | 0.9168 | 0.9813 |
| 0.0507 | 2.0 | 1756 | 0.0608 | 0.9208 | 0.9346 | 0.9276 | 0.9835 |
| 0.03 | 3.0 | 2634 | 0.0611 | 0.9272 | 0.9382 | 0.9327 | 0.9843 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- 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": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9843042559613643}}]}]}
|
token-classification
|
codingJacob/distilbert-base-uncased-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"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 #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: 0.0611
* Precision: 0.9272
* Recall: 0.9382
* F1: 0.9327
* Accuracy: 0.9843
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 4.9.1
* Pytorch 1.9.0+cu102
* Datasets 1.10.2
* 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 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3"
] |
[
65,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3### Training results### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\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: 0.0605
- Precision: 0.9251
- Recall: 0.9357
- F1: 0.9304
- Accuracy: 0.9837
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2402 | 1.0 | 878 | 0.0694 | 0.9168 | 0.9215 | 0.9191 | 0.9814 |
| 0.051 | 2.0 | 1756 | 0.0595 | 0.9249 | 0.9330 | 0.9289 | 0.9833 |
| 0.0302 | 3.0 | 2634 | 0.0605 | 0.9251 | 0.9357 | 0.9304 | 0.9837 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- 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": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9837323462595516}}]}]}
|
token-classification
|
cogito233/distilbert-base-uncased-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"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 #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: 0.0605
* Precision: 0.9251
* Recall: 0.9357
* F1: 0.9304
* Accuracy: 0.9837
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 4.9.2
* 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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
65,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# LaBSE for English and Russian
This is a truncated version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is, in turn, a port of [LaBSE](https://tfhub.dev/google/LaBSE/1) by Google.
The current model has only English and Russian tokens left in the vocabulary.
Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings.
To get the sentence embeddings, you can use the following code:
```python
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cointegrated/LaBSE-en-ru")
model = AutoModel.from_pretrained("cointegrated/LaBSE-en-ru")
sentences = ["Hello World", "Привет Мир"]
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = model_output.pooler_output
embeddings = torch.nn.functional.normalize(embeddings)
print(embeddings)
```
The model has been truncated in [this notebook](https://colab.research.google.com/drive/1dnPRn0-ugj3vZgSpyCC9sgslM2SuSfHy?usp=sharing).
You can adapt it for other languages (like [EIStakovskii/LaBSE-fr-de](https://huggingface.co/EIStakovskii/LaBSE-fr-de)), models or datasets.
## Reference:
Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. [Language-agnostic BERT Sentence Embedding](https://arxiv.org/abs/2007.01852). July 2020
License: [https://tfhub.dev/google/LaBSE/1](https://tfhub.dev/google/LaBSE/1)
|
{"language": ["ru", "en"], "tags": ["feature-extraction", "embeddings", "sentence-similarity"]}
|
feature-extraction
|
cointegrated/LaBSE-en-ru
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"pretraining",
"feature-extraction",
"embeddings",
"sentence-similarity",
"ru",
"en",
"arxiv:2007.01852",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2007.01852"
] |
[
"ru",
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #bert #pretraining #feature-extraction #embeddings #sentence-similarity #ru #en #arxiv-2007.01852 #endpoints_compatible #has_space #region-us
|
# LaBSE for English and Russian
This is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google.
The current model has only English and Russian tokens left in the vocabulary.
Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings.
To get the sentence embeddings, you can use the following code:
The model has been truncated in this notebook.
You can adapt it for other languages (like EIStakovskii/LaBSE-fr-de), models or datasets.
## Reference:
Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. Language-agnostic BERT Sentence Embedding. July 2020
License: URL
|
[
"# LaBSE for English and Russian\nThis is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google.\n\nThe current model has only English and Russian tokens left in the vocabulary.\nThus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings.\n \nTo get the sentence embeddings, you can use the following code:\n\n\nThe model has been truncated in this notebook.\nYou can adapt it for other languages (like EIStakovskii/LaBSE-fr-de), models or datasets.",
"## Reference:\nFangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. Language-agnostic BERT Sentence Embedding. July 2020\n\nLicense: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #bert #pretraining #feature-extraction #embeddings #sentence-similarity #ru #en #arxiv-2007.01852 #endpoints_compatible #has_space #region-us \n",
"# LaBSE for English and Russian\nThis is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google.\n\nThe current model has only English and Russian tokens left in the vocabulary.\nThus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings.\n \nTo get the sentence embeddings, you can use the following code:\n\n\nThe model has been truncated in this notebook.\nYou can adapt it for other languages (like EIStakovskii/LaBSE-fr-de), models or datasets.",
"## Reference:\nFangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. Language-agnostic BERT Sentence Embedding. July 2020\n\nLicense: URL"
] |
[
67,
156,
44
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #pretraining #feature-extraction #embeddings #sentence-similarity #ru #en #arxiv-2007.01852 #endpoints_compatible #has_space #region-us \n# LaBSE for English and Russian\nThis is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google.\n\nThe current model has only English and Russian tokens left in the vocabulary.\nThus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings.\n \nTo get the sentence embeddings, you can use the following code:\n\n\nThe model has been truncated in this notebook.\nYou can adapt it for other languages (like EIStakovskii/LaBSE-fr-de), models or datasets.## Reference:\nFangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. Language-agnostic BERT Sentence Embedding. July 2020\n\nLicense: URL"
] |
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] |
null | null |
transformers
|
This is a RoBERTa-large classifier trained on the CoLA corpus [Warstadt et al., 2019](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00290),
which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for evaluation of text style transfer.
The model was trained in the paper [Krishna et al, 2020. Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700), and its original version is available at [their project page](http://style.cs.umass.edu). We converted this model from Fairseq to Transformers format. All credit goes to the authors of the original paper.
## Citation
If you found this model useful and refer to it, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-classification
|
cointegrated/roberta-large-cola-krishna2020
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
|
This is a RoBERTa-large classifier trained on the CoLA corpus Warstadt et al., 2019,
which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for evaluation of text style transfer.
The model was trained in the paper Krishna et al, 2020. Reformulating Unsupervised Style Transfer as Paraphrase Generation, and its original version is available at their project page. We converted this model from Fairseq to Transformers format. All credit goes to the authors of the original paper.
If you found this model useful and refer to it, please cite the original work:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
50
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a version of paraphrase detector by DeepPavlov ([details in the documentation](http://docs.deeppavlov.ai/en/master/features/overview.html#ranking-model-docs)) ported to the `Transformers` format.
All credit goes to the authors of DeepPavlov.
The model has been trained on the dataset from http://paraphraser.ru/.
It classifies texts as paraphrases (class 1) or non-paraphrases (class 0).
```python
import torch
from transformers import AutoModelForSequenceClassification, BertTokenizer
model_name = 'cointegrated/rubert-base-cased-dp-paraphrase-detection'
model = AutoModelForSequenceClassification.from_pretrained(model_name).cuda()
tokenizer = BertTokenizer.from_pretrained(model_name)
def compare_texts(text1, text2):
batch = tokenizer(text1, text2, return_tensors='pt').to(model.device)
with torch.inference_mode():
proba = torch.softmax(model(**batch).logits, -1).cpu().numpy()
return proba[0] # p(non-paraphrase), p(paraphrase)
print(compare_texts('Сегодня на улице хорошая погода', 'Сегодня на улице отвратительная погода'))
# [0.7056226 0.2943774]
print(compare_texts('Сегодня на улице хорошая погода', 'Отличная погодка сегодня выдалась'))
# [0.16524374 0.8347562 ]
```
P.S. In the DeepPavlov repository, the tokenizer uses `max_seq_length=64`.
This model, however, uses `model_max_length=512`.
Therefore, the results on long texts may be inadequate.
|
{"language": ["ru"], "tags": ["sentence-similarity", "text-classification"], "datasets": ["merionum/ru_paraphraser"]}
|
text-classification
|
cointegrated/rubert-base-cased-dp-paraphrase-detection
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sentence-similarity",
"ru",
"dataset:merionum/ru_paraphraser",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us
|
This is a version of paraphrase detector by DeepPavlov (details in the documentation) ported to the 'Transformers' format.
All credit goes to the authors of DeepPavlov.
The model has been trained on the dataset from URL
It classifies texts as paraphrases (class 1) or non-paraphrases (class 0).
P.S. In the DeepPavlov repository, the tokenizer uses 'max_seq_length=64'.
This model, however, uses 'model_max_length=512'.
Therefore, the results on long texts may be inadequate.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
63
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sentence-similarity #ru #dataset-merionum/ru_paraphraser #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# RuBERT for NLI (natural language inference)
This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
## Usage
How to run the model for NLI:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-base-cased-nli-threeway'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
text1 = 'Сократ - человек, а все люди смертны.'
text2 = 'Сократ никогда не умрёт.'
with torch.inference_mode():
out = model(**tokenizer(text1, text2, return_tensors='pt').to(model.device))
proba = torch.softmax(out.logits, -1).cpu().numpy()[0]
print({v: proba[k] for k, v in model.config.id2label.items()})
# {'entailment': 0.009525929, 'contradiction': 0.9332064, 'neutral': 0.05726764}
```
You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis:
```python
def predict_zero_shot(text, label_texts, model, tokenizer, label='entailment', normalize=True):
label_texts
tokens = tokenizer([text] * len(label_texts), label_texts, truncation=True, return_tensors='pt', padding=True)
with torch.inference_mode():
result = torch.softmax(model(**tokens.to(model.device)).logits, -1)
proba = result[:, model.config.label2id[label]].cpu().numpy()
if normalize:
proba /= sum(proba)
return proba
classes = ['Я доволен', 'Я недоволен']
predict_zero_shot('Какая гадость эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.05609814, 0.9439019 ], dtype=float32)
predict_zero_shot('Какая вкусная эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.9059292 , 0.09407079], dtype=float32)
```
Alternatively, you can use [Huggingface pipelines](https://huggingface.co/transformers/main_classes/pipelines.html) for inference.
## Sources
The model has been trained on a series of NLI datasets automatically translated to Russian from English.
Most datasets were taken [from the repo of Felipe Salvatore](https://github.com/felipessalvatore/NLI_datasets):
[JOCI](https://github.com/sheng-z/JOCI),
[MNLI](https://cims.nyu.edu/~sbowman/multinli/),
[MPE](https://aclanthology.org/I17-1011/),
[SICK](http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf),
[SNLI](https://nlp.stanford.edu/projects/snli/).
Some datasets obtained from the original sources:
[ANLI](https://github.com/facebookresearch/anli),
[NLI-style FEVER](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md),
[IMPPRES](https://github.com/facebookresearch/Imppres).
## Performance
The table below shows ROC AUC (one class vs rest) for five models on the corresponding *dev* sets:
- [tiny](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli): a small BERT predicting entailment vs not_entailment
- [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway): a base-sized BERT predicting entailment vs not_entailment
- [threeway](https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway) (**this model**): a base-sized BERT predicting entailment vs contradiction vs neutral
- [vicgalle-xlm](https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli): a large multilingual NLI model
- [facebook-bart](https://huggingface.co/facebook/bart-large-mnli): a large multilingual NLI model
|model |add_one_rte|anli_r1|anli_r2|anli_r3|copa|fever|help|iie |imppres|joci|mnli |monli|mpe |scitail|sick|snli|terra|total |
|------------------------|-----------|-------|-------|-------|----|-----|----|-----|-------|----|-----|-----|----|-------|----|----|-----|------|
|n_observations |387 |1000 |1000 |1200 |200 |20474|3355|31232|7661 |939 |19647|269 |1000|2126 |500 |9831|307 |101128|
|tiny/entailment |0.77 |0.59 |0.52 |0.53 |0.53|0.90 |0.81|0.78 |0.93 |0.81|0.82 |0.91 |0.81|0.78 |0.93|0.95|0.67 |0.77 |
|twoway/entailment |0.89 |0.73 |0.61 |0.62 |0.58|0.96 |0.92|0.87 |0.99 |0.90|0.90 |0.99 |0.91|0.96 |0.97|0.97|0.87 |0.86 |
|threeway/entailment |0.91 |0.75 |0.61 |0.61 |0.57|0.96 |0.56|0.61 |0.99 |0.90|0.91 |0.67 |0.92|0.84 |0.98|0.98|0.90 |0.80 |
|vicgalle-xlm/entailment |0.88 |0.79 |0.63 |0.66 |0.57|0.93 |0.56|0.62 |0.77 |0.80|0.90 |0.70 |0.83|0.84 |0.91|0.93|0.93 |0.78 |
|facebook-bart/entailment|0.51 |0.41 |0.43 |0.47 |0.50|0.74 |0.55|0.57 |0.60 |0.63|0.70 |0.52 |0.56|0.68 |0.67|0.72|0.64 |0.58 |
|threeway/contradiction | |0.71 |0.64 |0.61 | |0.97 | | |1.00 |0.77|0.92 | |0.89| |0.99|0.98| |0.85 |
|threeway/neutral | |0.79 |0.70 |0.62 | |0.91 | | |0.99 |0.68|0.86 | |0.79| |0.96|0.96| |0.83 |
For evaluation (and for training of the [tiny](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli) and [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway) models), some extra datasets were used:
[Add-one RTE](https://cs.brown.edu/people/epavlick/papers/ans.pdf),
[CoPA](https://people.ict.usc.edu/~gordon/copa.html),
[IIE](https://aclanthology.org/I17-1100), and
[SCITAIL](https://allenai.org/data/scitail) taken from [the repo of Felipe Salvatore](https://github.com/felipessalvatore/NLI_datasets) and translatted,
[HELP](https://github.com/verypluming/HELP) and [MoNLI](https://github.com/atticusg/MoNLI) taken from the original sources and translated,
and Russian [TERRa](https://russiansuperglue.com/ru/tasks/task_info/TERRa).
|
{"language": "ru", "tags": ["rubert", "russian", "nli", "rte", "zero-shot-classification"], "datasets": ["cointegrated/nli-rus-translated-v2021"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u042f \u0445\u043e\u0447\u0443 \u043f\u043e\u0435\u0445\u0430\u0442\u044c \u0432 \u0410\u0432\u0441\u0442\u0440\u0430\u043b\u0438\u044e", "candidate_labels": "\u0441\u043f\u043e\u0440\u0442,\u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u044f,\u043c\u0443\u0437\u044b\u043a\u0430,\u043a\u0438\u043d\u043e,\u043a\u043d\u0438\u0433\u0438,\u043d\u0430\u0443\u043a\u0430,\u043f\u043e\u043b\u0438\u0442\u0438\u043a\u0430", "hypothesis_template": "\u0422\u0435\u043c\u0430 \u0442\u0435\u043a\u0441\u0442\u0430 - {}."}]}
|
zero-shot-classification
|
cointegrated/rubert-base-cased-nli-threeway
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification",
"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
RuBERT for NLI (natural language inference)
===========================================
This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
Usage
-----
How to run the model for NLI:
You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis:
Alternatively, you can use Huggingface pipelines for inference.
Sources
-------
The model has been trained on a series of NLI datasets automatically translated to Russian from English.
Most datasets were taken from the repo of Felipe Salvatore:
JOCI,
MNLI,
MPE,
SICK,
SNLI.
Some datasets obtained from the original sources:
ANLI,
NLI-style FEVER,
IMPPRES.
Performance
-----------
The table below shows ROC AUC (one class vs rest) for five models on the corresponding *dev* sets:
* tiny: a small BERT predicting entailment vs not\_entailment
* twoway: a base-sized BERT predicting entailment vs not\_entailment
* threeway (this model): a base-sized BERT predicting entailment vs contradiction vs neutral
* vicgalle-xlm: a large multilingual NLI model
* facebook-bart: a large multilingual NLI model
For evaluation (and for training of the tiny and twoway models), some extra datasets were used:
Add-one RTE,
CoPA,
IIE, and
SCITAIL taken from the repo of Felipe Salvatore and translatted,
HELP and MoNLI taken from the original sources and translated,
and Russian TERRa.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
84
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# RuBERT for NLI (natural language inference)
This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a similar model: https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway
|
{"language": "ru", "tags": ["rubert", "russian", "nli", "rte", "zero-shot-classification"], "datasets": ["cointegrated/nli-rus-translated-v2021"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u042f \u0445\u043e\u0447\u0443 \u043f\u043e\u0435\u0445\u0430\u0442\u044c \u0432 \u0410\u0432\u0441\u0442\u0440\u0430\u043b\u0438\u044e", "candidate_labels": "\u0441\u043f\u043e\u0440\u0442,\u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u044f,\u043c\u0443\u0437\u044b\u043a\u0430,\u043a\u0438\u043d\u043e,\u043a\u043d\u0438\u0433\u0438,\u043d\u0430\u0443\u043a\u0430,\u043f\u043e\u043b\u0438\u0442\u0438\u043a\u0430", "hypothesis_template": "\u0422\u0435\u043c\u0430 \u0442\u0435\u043a\u0441\u0442\u0430 - {}."}]}
|
zero-shot-classification
|
cointegrated/rubert-base-cased-nli-twoway
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification",
"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us
|
# RuBERT for NLI (natural language inference)
This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a similar model: URL
|
[
"# RuBERT for NLI (natural language inference)\n\nThis is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a similar model: URL"
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RuBERT for NLI (natural language inference)\n\nThis is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a similar model: URL"
] |
[
80,
66
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us \n# RuBERT for NLI (natural language inference)\n\nThis is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a similar model: URL"
] |
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] |
null | null |
transformers
|
The model for https://github.com/Lesha17/Punctuation; all credits go to the owner of this repository.
|
{}
|
token-classification
|
cointegrated/rubert-base-lesha17-punctuation
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
The model for URL all credits go to the owner of this repository.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
42
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# RuBERT-tiny for NLI (natural language inference)
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a related model: https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway
|
{"language": "ru", "tags": ["rubert", "russian", "nli", "rte", "zero-shot-classification"], "datasets": ["cointegrated/nli-rus-translated-v2021"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u0421\u0435\u0440\u0432\u0438\u0441 \u043e\u0442\u0441\u0442\u043e\u0439\u043d\u044b\u0439, \u043a\u043e\u0440\u043c\u0438\u043b\u0438 \u043d\u0435\u0432\u043a\u0443\u0441\u043d\u043e", "candidate_labels": "\u041c\u043d\u0435 \u043f\u043e\u043d\u0440\u0430\u0432\u0438\u043b\u043e\u0441\u044c, \u041c\u043d\u0435 \u043d\u0435 \u043f\u043e\u043d\u0440\u0430\u0432\u0438\u043b\u043e\u0441\u044c", "hypothesis_template": "{}."}]}
|
zero-shot-classification
|
cointegrated/rubert-tiny-bilingual-nli
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification",
"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us
|
# RuBERT-tiny for NLI (natural language inference)
This is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.
For more details, see the card for a related model: URL
|
[
"# RuBERT-tiny for NLI (natural language inference)\n\nThis is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a related model: URL"
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RuBERT-tiny for NLI (natural language inference)\n\nThis is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a related model: URL"
] |
[
80,
65
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #rubert #russian #nli #rte #zero-shot-classification #ru #dataset-cointegrated/nli-rus-translated-v2021 #autotrain_compatible #endpoints_compatible #region-us \n# RuBERT-tiny for NLI (natural language inference)\n\nThis is the cointegrated/rubert-tiny model fine-tuned to predict the logical relationship between two short texts: entailment or not entailment.\n\nFor more details, see the card for a related model: URL"
] |
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] |
null | null |
transformers
|
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts.
The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`.
## Usage
The function below estimates the sentiment of the given text:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-tiny-sentiment-balanced'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
def get_sentiment(text, return_type='label'):
""" Calculate sentiment of a text. `return_type` can be 'label', 'score' or 'proba' """
with torch.no_grad():
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()[0]
if return_type == 'label':
return model.config.id2label[proba.argmax()]
elif return_type == 'score':
return proba.dot([-1, 0, 1])
return proba
text = 'Какая гадость эта ваша заливная рыба!'
# classify the text
print(get_sentiment(text, 'label')) # negative
# score the text on the scale from -1 (very negative) to +1 (very positive)
print(get_sentiment(text, 'score')) # -0.5894946306943893
# calculate probabilities of all labels
print(get_sentiment(text, 'proba')) # [0.7870447 0.4947824 0.19755007]
```
## Training
We trained the model on [the datasets collected by Smetanin](https://github.com/sismetanin/sentiment-analysis-in-russian). We have converted all training data into a 3-class format and have up- and downsampled the training data to balance both the sources and the classes. The training code is available as [a Colab notebook](https://gist.github.com/avidale/e678c5478086c1d1adc52a85cb2b93e6). The metrics on the balanced test set are the following:
| Source | Macro F1 |
| ----------- | ----------- |
| SentiRuEval2016_banks | 0.83 |
| SentiRuEval2016_tele | 0.74 |
| kaggle_news | 0.66 |
| linis | 0.50 |
| mokoron | 0.98 |
| rureviews | 0.72 |
| rusentiment | 0.67 |
|
{"language": ["ru"], "tags": ["russian", "classification", "sentiment", "multiclass"], "widget": [{"text": "\u041a\u0430\u043a\u0430\u044f \u0433\u0430\u0434\u043e\u0441\u0442\u044c \u044d\u0442\u0430 \u0432\u0430\u0448\u0430 \u0437\u0430\u043b\u0438\u0432\u043d\u0430\u044f \u0440\u044b\u0431\u0430!"}]}
|
text-classification
|
cointegrated/rubert-tiny-sentiment-balanced
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"russian",
"classification",
"sentiment",
"multiclass",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us
|
This is the cointegrated/rubert-tiny model fine-tuned for classification of sentiment for short Russian texts.
The problem is formulated as multiclass classification: 'negative' vs 'neutral' vs 'positive'.
Usage
-----
The function below estimates the sentiment of the given text:
Training
--------
We trained the model on the datasets collected by Smetanin. We have converted all training data into a 3-class format and have up- and downsampled the training data to balance both the sources and the classes. The training code is available as a Colab notebook. The metrics on the balanced test set are the following:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
55
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks.
The problem is formulated as multilabel classification with the following classes:
- `non-toxic`: the text does NOT contain insults, obscenities, and threats, in the sense of the [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) competition.
- `insult`
- `obscenity`
- `threat`
- `dangerous`: the text is inappropriate, in the sense of [Babakov et.al.](https://arxiv.org/abs/2103.05345), i.e. it can harm the reputation of the speaker.
A text can be considered safe if it is BOTH `non-toxic` and NOT `dangerous`.
## Usage
The function below estimates the probability that the text is either toxic OR dangerous:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-tiny-toxicity'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
def text2toxicity(text, aggregate=True):
""" Calculate toxicity of a text (if aggregate=True) or a vector of toxicity aspects (if aggregate=False)"""
with torch.no_grad():
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()
if isinstance(text, str):
proba = proba[0]
if aggregate:
return 1 - proba.T[0] * (1 - proba.T[-1])
return proba
print(text2toxicity('я люблю нигеров', True))
# 0.9350118728093193
print(text2toxicity('я люблю нигеров', False))
# [0.9715758 0.0180863 0.0045551 0.00189755 0.9331106 ]
print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], True))
# [0.93501186 0.04156357]
print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], False))
# [[9.7157580e-01 1.8086294e-02 4.5550885e-03 1.8975559e-03 9.3311059e-01]
# [9.9979788e-01 1.9048342e-04 1.5297388e-04 1.7452303e-04 4.1369814e-02]]
```
## Training
The model has been trained on the joint dataset of [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) and [Babakov et.al.](https://arxiv.org/abs/2103.05345) with `Adam` optimizer, the learning rate of `1e-5`, and batch size of `64` for `15` epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is:
```
non-toxic : 0.9937
insult : 0.9912
obscenity : 0.9881
threat : 0.9910
dangerous : 0.8295
```
|
{"language": ["ru"], "tags": ["russian", "classification", "toxicity", "multilabel"], "widget": [{"text": "\u0418\u0434\u0438 \u0442\u044b \u043d\u0430\u0444\u0438\u0433!"}]}
|
text-classification
|
cointegrated/rubert-tiny-toxicity
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"russian",
"classification",
"toxicity",
"multilabel",
"ru",
"arxiv:2103.05345",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2103.05345"
] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #toxicity #multilabel #ru #arxiv-2103.05345 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is the cointegrated/rubert-tiny model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks.
The problem is formulated as multilabel classification with the following classes:
- 'non-toxic': the text does NOT contain insults, obscenities, and threats, in the sense of the OK ML Cup competition.
- 'insult'
- 'obscenity'
- 'threat'
- 'dangerous': the text is inappropriate, in the sense of Babakov URL., i.e. it can harm the reputation of the speaker.
A text can be considered safe if it is BOTH 'non-toxic' and NOT 'dangerous'.
## Usage
The function below estimates the probability that the text is either toxic OR dangerous:
## Training
The model has been trained on the joint dataset of OK ML Cup and Babakov URL. with 'Adam' optimizer, the learning rate of '1e-5', and batch size of '64' for '15' epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is:
|
[
"## Usage\n\nThe function below estimates the probability that the text is either toxic OR dangerous:",
"## Training\n\nThe model has been trained on the joint dataset of OK ML Cup and Babakov URL. with 'Adam' optimizer, the learning rate of '1e-5', and batch size of '64' for '15' epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is:"
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #toxicity #multilabel #ru #arxiv-2103.05345 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Usage\n\nThe function below estimates the probability that the text is either toxic OR dangerous:",
"## Training\n\nThe model has been trained on the joint dataset of OK ML Cup and Babakov URL. with 'Adam' optimizer, the learning rate of '1e-5', and batch size of '64' for '15' epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is:"
] |
[
67,
21,
101
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #toxicity #multilabel #ru #arxiv-2103.05345 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Usage\n\nThe function below estimates the probability that the text is either toxic OR dangerous:## Training\n\nThe model has been trained on the joint dataset of OK ML Cup and Babakov URL. with 'Adam' optimizer, the learning rate of '1e-5', and batch size of '64' for '15' epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is:"
] |
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] |
null | null |
transformers
|
This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English (45 MB, 12M parameters). There is also an **updated version of this model**, [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2), with a larger vocabulary and better quality on practically all Russian NLU tasks.
This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its `[CLS]` embeddings can be used as a sentence representation aligned between Russian and English.
It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus), [OPUS-100](https://huggingface.co/datasets/opus100) and [Tatoeba](https://huggingface.co/datasets/tatoeba), using MLM loss (distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)), translation ranking loss, and `[CLS]` embeddings distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), [rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence), Laser and USE.
There is a more detailed [description in Russian](https://habr.com/ru/post/562064/).
Sentence embeddings can be produced as follows:
```python
# pip install transformers sentencepiece
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny")
model = AutoModel.from_pretrained("cointegrated/rubert-tiny")
# model.cuda() # uncomment it if you have a GPU
def embed_bert_cls(text, model, tokenizer):
t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**{k: v.to(model.device) for k, v in t.items()})
embeddings = model_output.last_hidden_state[:, 0, :]
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings[0].cpu().numpy()
print(embed_bert_cls('привет мир', model, tokenizer).shape)
# (312,)
```
|
{"language": ["ru", "en"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm", "tiny", "feature-extraction", "sentence-similarity"], "widget": [{"text": "\u041c\u0438\u043d\u0438\u0430\u0442\u044e\u0440\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f [MASK] \u0440\u0430\u0437\u043d\u044b\u0445 \u0437\u0430\u0434\u0430\u0447."}], "pipeline_tag": "fill-mask"}
|
fill-mask
|
cointegrated/rubert-tiny
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"pretraining",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"feature-extraction",
"sentence-similarity",
"ru",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru",
"en"
] |
TAGS
#transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #ru #en #license-mit #endpoints_compatible #has_space #region-us
|
This is a very small distilled version of the bert-base-multilingual-cased model for Russian and English (45 MB, 12M parameters). There is also an updated version of this model, rubert-tiny2, with a larger vocabulary and better quality on practically all Russian NLU tasks.
This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its '[CLS]' embeddings can be used as a sentence representation aligned between Russian and English.
It was trained on the Yandex Translate corpus, OPUS-100 and Tatoeba, using MLM loss (distilled from bert-base-multilingual-cased), translation ranking loss, and '[CLS]' embeddings distilled from LaBSE, rubert-base-cased-sentence, Laser and USE.
There is a more detailed description in Russian.
Sentence embeddings can be produced as follows:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #ru #en #license-mit #endpoints_compatible #has_space #region-us \n"
] |
[
76
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #ru #en #license-mit #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
This is the [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions.
The model on the [CEDR dataset](https://huggingface.co/datasets/cedr) described in the paper ["Data-Driven Model for Emotion Detection in Russian Texts"](https://doi.org/10.1016/j.procs.2021.06.075) by Sboev et al.
The model has been trained with Adam optimizer for 40 epochs with learning rate `1e-5` and batch size 64 [in this notebook](https://colab.research.google.com/drive/1AFW70EJaBn7KZKRClDIdDUpbD46cEsat?usp=sharing).
The quality of the predicted probabilities on the test dataset is the following:
| label | no emotion | joy |sadness |surprise| fear |anger | mean | mean (emotions) |
|----------|------------|--------|--------|--------|--------|--------| --------| ----------------|
| AUC | 0.9286 | 0.9512 | 0.9564 | 0.8908 | 0.8955 | 0.7511 | 0.8956 | 0.8890 |
| F1 micro | 0.8624 | 0.9389 | 0.9362 | 0.9469 | 0.9575 | 0.9261 | 0.9280 | 0.9411 |
| F1 macro | 0.8562 | 0.8962 | 0.9017 | 0.8366 | 0.8359 | 0.6820 | 0.8348 | 0.8305 |
|
{"language": ["ru"], "tags": ["russian", "classification", "sentiment", "emotion-classification", "multiclass"], "datasets": ["cedr"], "widget": [{"text": "\u0411\u0435\u0441\u0438\u0448\u044c \u043c\u0435\u043d\u044f, \u043f\u0430\u0434\u043b\u0430"}, {"text": "\u041a\u0430\u043a \u0437\u0434\u043e\u0440\u043e\u0432\u043e, \u0447\u0442\u043e \u0432\u0441\u0435 \u043c\u044b \u0437\u0434\u0435\u0441\u044c \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0441\u043e\u0431\u0440\u0430\u043b\u0438\u0441\u044c"}, {"text": "\u041a\u0430\u043a-\u0442\u043e \u0441\u0442\u0440\u0451\u043c\u043d\u043e, \u0434\u0430\u0432\u0430\u0439 \u0441\u0432\u0430\u043b\u0438\u043c \u043e\u0442\u0441\u044e\u0434\u0430?"}, {"text": "\u0413\u0440\u0443\u0441\u0442\u044c-\u0442\u043e\u0441\u043a\u0430 \u043c\u0435\u043d\u044f \u0441\u044a\u0435\u0434\u0430\u0435\u0442"}, {"text": "\u0414\u0430\u043d\u043d\u044b\u0439 \u0444\u0440\u0430\u0433\u043c\u0435\u043d\u0442 \u0442\u0435\u043a\u0441\u0442\u0430 \u043d\u0435 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u043e \u043d\u0438\u043a\u0430\u043a\u0438\u0445 \u044d\u043c\u043e\u0446\u0438\u0439"}, {"text": "\u041d\u0438\u0444\u0438\u0433\u0430 \u0441\u0435\u0431\u0435, \u043d\u0435\u0443\u0436\u0435\u043b\u0438 \u0442\u0430\u043a \u0442\u043e\u0436\u0435 \u0431\u044b\u0432\u0430\u0435\u0442!"}]}
|
text-classification
|
cointegrated/rubert-tiny2-cedr-emotion-detection
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"russian",
"classification",
"sentiment",
"emotion-classification",
"multiclass",
"ru",
"dataset:cedr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is the cointegrated/rubert-tiny2 model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions.
The model on the CEDR dataset described in the paper "Data-Driven Model for Emotion Detection in Russian Texts" by Sboev et al.
The model has been trained with Adam optimizer for 40 epochs with learning rate '1e-5' and batch size 64 in this notebook.
The quality of the predicted probabilities on the test dataset is the following:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
71
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
sentence-transformers
|
This is an updated version of [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny): a small Russian BERT-based encoder with high-quality sentence embeddings. This [post in Russian](https://habr.com/ru/post/669674/) gives more details.
The differences from the previous version include:
- a larger vocabulary: 83828 tokens instead of 29564;
- larger supported sequences: 2048 instead of 512;
- sentence embeddings approximate LaBSE closer than before;
- meaningful segment embeddings (tuned on the NLI task)
- the model is focused only on Russian.
The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task.
Sentence embeddings can be produced as follows:
```python
# pip install transformers sentencepiece
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
model = AutoModel.from_pretrained("cointegrated/rubert-tiny2")
# model.cuda() # uncomment it if you have a GPU
def embed_bert_cls(text, model, tokenizer):
t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**{k: v.to(model.device) for k, v in t.items()})
embeddings = model_output.last_hidden_state[:, 0, :]
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings[0].cpu().numpy()
print(embed_bert_cls('привет мир', model, tokenizer).shape)
# (312,)
```
Alternatively, you can use the model with `sentence_transformers`:
```Python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('cointegrated/rubert-tiny2')
sentences = ["привет мир", "hello world", "здравствуй вселенная"]
embeddings = model.encode(sentences)
print(embeddings)
```
|
{"language": ["ru"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm", "tiny", "feature-extraction", "sentence-similarity", "sentence-transformers", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"text": "\u041c\u0438\u043d\u0438\u0430\u0442\u044e\u0440\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f [MASK] \u0440\u0430\u0437\u043d\u044b\u0445 \u0437\u0430\u0434\u0430\u0447."}]}
|
sentence-similarity
|
cointegrated/rubert-tiny2
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"pretraining",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"feature-extraction",
"sentence-similarity",
"transformers",
"ru",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#sentence-transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #transformers #ru #license-mit #endpoints_compatible #has_space #region-us
|
This is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details.
The differences from the previous version include:
- a larger vocabulary: 83828 tokens instead of 29564;
- larger supported sequences: 2048 instead of 512;
- sentence embeddings approximate LaBSE closer than before;
- meaningful segment embeddings (tuned on the NLI task)
- the model is focused only on Russian.
The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task.
Sentence embeddings can be produced as follows:
Alternatively, you can use the model with 'sentence_transformers':
|
[] |
[
"TAGS\n#sentence-transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #transformers #ru #license-mit #endpoints_compatible #has_space #region-us \n"
] |
[
80
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #safetensors #bert #pretraining #russian #fill-mask #embeddings #masked-lm #tiny #feature-extraction #sentence-similarity #transformers #ru #license-mit #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
This is a model for abstractive Russian summarization, based on [cointegrated/rut5-base-multitask](https://huggingface.co/cointegrated/rut5-base-multitask) and fine-tuned on 4 datasets.
It can be used as follows:
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'cointegrated/rut5-base-absum'
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
model.cuda();
model.eval();
def summarize(
text, n_words=None, compression=None,
max_length=1000, num_beams=3, do_sample=False, repetition_penalty=10.0,
**kwargs
):
"""
Summarize the text
The following parameters are mutually exclusive:
- n_words (int) is an approximate number of words to generate.
- compression (float) is an approximate length ratio of summary and original text.
"""
if n_words:
text = '[{}] '.format(n_words) + text
elif compression:
text = '[{0:.1g}] '.format(compression) + text
x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
with torch.inference_mode():
out = model.generate(
**x,
max_length=max_length, num_beams=num_beams,
do_sample=do_sample, repetition_penalty=repetition_penalty,
**kwargs
)
return tokenizer.decode(out[0], skip_special_tokens=True)
text = """Высота башни составляет 324 метра (1063 фута), примерно такая же высота, как у 81-этажного здания, и самое высокое сооружение в Париже. Его основание квадратно, размером 125 метров (410 футов) с любой стороны. Во время строительства Эйфелева башня превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире, и этот титул она удерживала в течение 41 года до завершения строительство здания Крайслер в Нью-Йорке в 1930 году. Это первое сооружение которое достигло высоты 300 метров. Из-за добавления вещательной антенны на вершине башни в 1957 году она сейчас выше здания Крайслер на 5,2 метра (17 футов). За исключением передатчиков, Эйфелева башня является второй самой высокой отдельно стоящей структурой во Франции после виадука Мийо."""
print(summarize(text))
# Эйфелева башня достигла высоты 300 метров.
print(summarize(text, n_words=10))
# Французская Эйфелева башня достигла высоты 300 метров.
```
|
{"language": ["ru"], "license": "mit", "tags": ["russian", "summarization"], "datasets": ["IlyaGusev/gazeta", "csebuetnlp/xlsum", "mlsum", "wiki_lingua"], "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 324 \u043c\u0435\u0442\u0440\u0430 (1063 \u0444\u0443\u0442\u0430), \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0442\u0430\u043a\u0430\u044f \u0436\u0435 \u0432\u044b\u0441\u043e\u0442\u0430, \u043a\u0430\u043a \u0443 81-\u044d\u0442\u0430\u0436\u043d\u043e\u0433\u043e \u0437\u0434\u0430\u043d\u0438\u044f, \u0438 \u0441\u0430\u043c\u043e\u0435 \u0432\u044b\u0441\u043e\u043a\u043e\u0435 \u0441\u043e\u043e\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0432 \u041f\u0430\u0440\u0438\u0436\u0435. \u0415\u0433\u043e \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043d\u043e, \u0440\u0430\u0437\u043c\u0435\u0440\u043e\u043c 125 \u043c\u0435\u0442\u0440\u043e\u0432 (410 \u0444\u0443\u0442\u043e\u0432) \u0441 \u043b\u044e\u0431\u043e\u0439 \u0441\u0442\u043e\u0440\u043e\u043d\u044b. \u0412\u043e \u0432\u0440\u0435\u043c\u044f \u0441\u0442\u0440\u043e\u0438\u0442\u0435\u043b\u044c\u0441\u0442\u0432\u0430 \u042d\u0439\u0444\u0435\u043b\u0435\u0432\u0430 \u0431\u0430\u0448\u043d\u044f \u043f\u0440\u0435\u0432\u0437\u043e\u0448\u043b\u0430 \u043c\u043e\u043d\u0443\u043c\u0435\u043d\u0442 \u0412\u0430\u0448\u0438\u043d\u0433\u0442\u043e\u043d\u0430, \u0441\u0442\u0430\u0432 \u0441\u0430\u043c\u044b\u043c \u0432\u044b\u0441\u043e\u043a\u0438\u043c \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u043c \u0441\u043e\u043e\u0440\u0443\u0436\u0435\u043d\u0438\u0435\u043c \u0432 \u043c\u0438\u0440\u0435, \u0438 \u044d\u0442\u043e\u0442 \u0442\u0438\u0442\u0443\u043b \u043e\u043d\u0430 \u0443\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u043b\u0430 \u0432 \u0442\u0435\u0447\u0435\u043d\u0438\u0435 41 \u0433\u043e\u0434\u0430 \u0434\u043e \u0437\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u044f \u0441\u0442\u0440\u043e\u0438\u0442\u0435\u043b\u044c\u0441\u0442\u0432\u043e \u0437\u0434\u0430\u043d\u0438\u044f \u041a\u0440\u0430\u0439\u0441\u043b\u0435\u0440 \u0432 \u041d\u044c\u044e-\u0419\u043e\u0440\u043a\u0435 \u0432 1930 \u0433\u043e\u0434\u0443. \u042d\u0442\u043e \u043f\u0435\u0440\u0432\u043e\u0435 \u0441\u043e\u043e\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0434\u043e\u0441\u0442\u0438\u0433\u043b\u043e \u0432\u044b\u0441\u043e\u0442\u044b 300 \u043c\u0435\u0442\u0440\u043e\u0432. \u0418\u0437-\u0437\u0430 \u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0432\u0435\u0449\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u0430\u043d\u0442\u0435\u043d\u043d\u044b \u043d\u0430 \u0432\u0435\u0440\u0448\u0438\u043d\u0435 \u0431\u0430\u0448\u043d\u0438 \u0432 1957 \u0433\u043e\u0434\u0443 \u043e\u043d\u0430 \u0441\u0435\u0439\u0447\u0430\u0441 \u0432\u044b\u0448\u0435 \u0437\u0434\u0430\u043d\u0438\u044f \u041a\u0440\u0430\u0439\u0441\u043b\u0435\u0440 \u043d\u0430 5,2 \u043c\u0435\u0442\u0440\u0430 (17 \u0444\u0443\u0442\u043e\u0432). \u0417\u0430 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435\u043c \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u0447\u0438\u043a\u043e\u0432, \u042d\u0439\u0444\u0435\u043b\u0435\u0432\u0430 \u0431\u0430\u0448\u043d\u044f \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0430\u043c\u043e\u0439 \u0432\u044b\u0441\u043e\u043a\u043e\u0439 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e \u0441\u0442\u043e\u044f\u0449\u0435\u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u0439 \u0432\u043e \u0424\u0440\u0430\u043d\u0446\u0438\u0438 \u043f\u043e\u0441\u043b\u0435 \u0432\u0438\u0430\u0434\u0443\u043a\u0430 \u041c\u0438\u0439\u043e."}]}
|
summarization
|
cointegrated/rut5-base-absum
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"russian",
"summarization",
"ru",
"dataset:IlyaGusev/gazeta",
"dataset:csebuetnlp/xlsum",
"dataset:mlsum",
"dataset:wiki_lingua",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #russian #summarization #ru #dataset-IlyaGusev/gazeta #dataset-csebuetnlp/xlsum #dataset-mlsum #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a model for abstractive Russian summarization, based on cointegrated/rut5-base-multitask and fine-tuned on 4 datasets.
It can be used as follows:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #summarization #ru #dataset-IlyaGusev/gazeta #dataset-csebuetnlp/xlsum #dataset-mlsum #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
108
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #summarization #ru #dataset-IlyaGusev/gazeta #dataset-csebuetnlp/xlsum #dataset-mlsum #dataset-wiki_lingua #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) with only some Rusian and English embeddings left.
More details are given in a Russian post: https://habr.com/ru/post/581932/
The model has been fine-tuned for several tasks with sentences or short paragraphs:
* translation (`translate ru-en` and `translate en-ru`)
* Paraphrasing (`paraphrase`)
* Filling gaps in a text (`fill`). The gaps can be denoted as `___` or `_3_`, where `3` is the approximate number of words that should be inserted.
* Restoring the text from a noisy bag of words (`assemble`)
* Simplification of texts (`simplify`)
* Dialogue response generation (`reply` based on fiction and `answer` based on online forums)
* Open-book question answering (`comprehend`)
* Asking questions about a text (`ask`)
* News title generation (`headline`)
For each task, the task name is joined with the input text by the ` | ` separator.
The model can be run with the following code:
```
# !pip install transformers sentencepiece
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-base-multitask")
model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-base-multitask")
def generate(text, **kwargs):
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
hypotheses = model.generate(**inputs, num_beams=5, **kwargs)
return tokenizer.decode(hypotheses[0], skip_special_tokens=True)
```
The model can be applied to each of the pretraining tasks:
```
print(generate('translate ru-en | Каждый охотник желает знать, где сидит фазан.'))
# Each hunter wants to know, where he is.
print(generate('paraphrase | Каждый охотник желает знать, где сидит фазан.',
encoder_no_repeat_ngram_size=1, repetition_penalty=0.5, no_repeat_ngram_size=1))
# В любом случае каждый рыбак мечтает познакомиться со своей фермой
print(generate('fill | Каждый охотник _3_, где сидит фазан.'))
# смотрит на озеро
print(generate('assemble | охотник каждый знать фазан сидит'))
# Каждый охотник знает, что фазан сидит.
print(generate('simplify | Местным продуктом-специалитетом с защищённым географическим наименованием по происхождению считается люнебургский степной барашек.', max_length=32))
# Местным продуктом-специалитетом считается люнебургский степной барашек.
print(generate('reply | Помогите мне закадрить девушку'))
# Что я хочу?
print(generate('answer | Помогите мне закадрить девушку'))
# я хочу познакомиться с девушкой!!!!!!!!
print(generate("comprehend | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, "
"прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо. Вопрос: откуда приехал Морган?"))
# из Австралии
print(generate("ask | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, "
"прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо.", max_length=32))
# Что разворачивается на фоне земельного конфликта между владельцами овец и ранчеро?
print(generate("headline | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, "
"прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо.", max_length=32))
# На фоне земельного конфликта разворачивается история любви овцевода Моргана Лейна и Марии Синглетон
```
However, it is strongly recommended that you fine tune the model for your own task.
|
{"language": ["ru", "en"], "license": "mit", "tags": ["russian"], "widget": [{"text": "fill | \u041f\u043e\u0447\u0435\u043c\u0443 \u043e\u043d\u0438 \u043d\u0435 ___ \u043d\u0430 \u043c\u0435\u043d\u044f?"}]}
|
text2text-generation
|
cointegrated/rut5-base-multitask
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"russian",
"ru",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru",
"en"
] |
TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a smaller version of the google/mt5-base with only some Rusian and English embeddings left.
More details are given in a Russian post: URL
The model has been fine-tuned for several tasks with sentences or short paragraphs:
* translation ('translate ru-en' and 'translate en-ru')
* Paraphrasing ('paraphrase')
* Filling gaps in a text ('fill'). The gaps can be denoted as '___' or '_3_', where '3' is the approximate number of words that should be inserted.
* Restoring the text from a noisy bag of words ('assemble')
* Simplification of texts ('simplify')
* Dialogue response generation ('reply' based on fiction and 'answer' based on online forums)
* Open-book question answering ('comprehend')
* Asking questions about a text ('ask')
* News title generation ('headline')
For each task, the task name is joined with the input text by the ' | ' separator.
The model can be run with the following code:
The model can be applied to each of the pretraining tasks:
However, it is strongly recommended that you fine tune the model for your own task.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
72
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This is a paraphraser for Russian sentences described [in this Habr post](https://habr.com/ru/post/564916/).
It is recommended to use the model with the `encoder_no_repeat_ngram_size` argument:
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'cointegrated/rut5-base-paraphraser'
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
model.cuda();
model.eval();
def paraphrase(text, beams=5, grams=4, do_sample=False):
x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
max_size = int(x.input_ids.shape[1] * 1.5 + 10)
out = model.generate(**x, encoder_no_repeat_ngram_size=grams, num_beams=beams, max_length=max_size, do_sample=do_sample)
return tokenizer.decode(out[0], skip_special_tokens=True)
print(paraphrase('Каждый охотник желает знать, где сидит фазан.'))
# Все охотники хотят знать где фазан сидит.
```
|
{"language": ["ru"], "license": "mit", "tags": ["russian", "paraphrasing", "paraphraser", "paraphrase"], "datasets": ["cointegrated/ru-paraphrase-NMT-Leipzig"], "widget": [{"text": "\u041a\u0430\u0436\u0434\u044b\u0439 \u043e\u0445\u043e\u0442\u043d\u0438\u043a \u0436\u0435\u043b\u0430\u0435\u0442 \u0437\u043d\u0430\u0442\u044c, \u0433\u0434\u0435 \u0441\u0438\u0434\u0438\u0442 \u0444\u0430\u0437\u0430\u043d."}]}
|
text2text-generation
|
cointegrated/rut5-base-paraphraser
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"russian",
"paraphrasing",
"paraphraser",
"paraphrase",
"ru",
"dataset:cointegrated/ru-paraphrase-NMT-Leipzig",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #russian #paraphrasing #paraphraser #paraphrase #ru #dataset-cointegrated/ru-paraphrase-NMT-Leipzig #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This is a paraphraser for Russian sentences described in this Habr post.
It is recommended to use the model with the 'encoder_no_repeat_ngram_size' argument:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #paraphrasing #paraphraser #paraphrase #ru #dataset-cointegrated/ru-paraphrase-NMT-Leipzig #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
99
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #russian #paraphrasing #paraphraser #paraphrase #ru #dataset-cointegrated/ru-paraphrase-NMT-Leipzig #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left.
* The original model has 582M parameters, with 384M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 30K (top 10K English and top 20K Russian tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
The creation of this model is described in the post [How to adapt a multilingual T5 model for a single language](https://cointegrated.medium.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) along with the source code.
|
{"language": ["ru", "en", "multilingual"], "license": "mit", "tags": ["russian"]}
|
text2text-generation
|
cointegrated/rut5-base
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"russian",
"ru",
"en",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru",
"en",
"multilingual"
] |
TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a smaller version of the google/mt5-base model with only Russian and some English embeddings left.
* The original model has 582M parameters, with 384M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 30K (top 10K English and top 20K Russian tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
The creation of this model is described in the post How to adapt a multilingual T5 model for a single language along with the source code.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
72
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #russian #ru #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This is a version of the [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) model fine-tuned on some Russian dialogue data. It is not very smart and creative, but it is small and fast, and can serve as a fallback response generator for some chatbot or can be fine-tuned to imitate the style of someone.
The input of the model is the previous dialogue utterances separated by `'\n\n'`, and the output is the next utterance.
The model can be used as follows:
```
# !pip install transformers sentencepiece
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small-chitchat")
model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small-chitchat")
text = 'Привет! Расскажи, как твои дела?'
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
hypotheses = model.generate(
**inputs,
do_sample=True, top_p=0.5, num_return_sequences=3,
repetition_penalty=2.5,
max_length=32,
)
for h in hypotheses:
print(tokenizer.decode(h, skip_special_tokens=True))
# Как обычно.
# Сейчас - в порядке.
# Хорошо.
# Wall time: 363 ms
```
|
{"language": "ru", "license": "mit", "tags": ["dialogue", "russian"]}
|
text2text-generation
|
cointegrated/rut5-small-chitchat
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"dialogue",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #dialogue #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a version of the cointegrated/rut5-small model fine-tuned on some Russian dialogue data. It is not very smart and creative, but it is small and fast, and can serve as a fallback response generator for some chatbot or can be fine-tuned to imitate the style of someone.
The input of the model is the previous dialogue utterances separated by ''\n\n'', and the output is the next utterance.
The model can be used as follows:
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #dialogue #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
67
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #dialogue #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
A version of https://huggingface.co/cointegrated/rut5-small-chitchat which is more dull but less toxic.
|
{}
|
text2text-generation
|
cointegrated/rut5-small-chitchat2
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
A version of URL which is more dull but less toxic.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
53
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences.
This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrated/rut5-small) model on the task of reconstructing a sentence:
* restoring word positions (after slightly shuffling them)
* restoring dropped words and punctuation marks (after dropping some of them randomly)
* restoring inflection of words (after changing their inflection randomly using [natasha](https://github.com/natasha/natasha) and [pymorphy2](https://github.com/kmike/pymorphy2) packages)
The fine-tuning was performed on a [Leipzig web corpus](https://wortschatz.uni-leipzig.de/en/download/Russian) of Russian sentences.
The model can be applied as follows:
```
# !pip install transformers sentencepiece
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small-normalizer")
model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small-normalizer")
text = 'меня тобой не понимать'
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
hypotheses = model.generate(
**inputs,
do_sample=True, top_p=0.95,
num_return_sequences=5,
repetition_penalty=2.5,
max_length=32,
)
for h in hypotheses:
print(tokenizer.decode(h, skip_special_tokens=True))
```
A possible output is:
```
# Мне тебя не понимать.
# Если бы ты понимаешь меня?
# Я с тобой не понимаю.
# Я тебя не понимаю.
# Я не понимаю о чем ты.
```
|
{"language": "ru", "license": "mit", "tags": ["normalization", "denoising autoencoder", "russian"], "widget": [{"text": "\u043c\u0435\u043d\u044f \u0442\u043e\u0431\u043e\u0439 \u043d\u0435 \u043f\u043e\u043d\u0438\u043c\u0430\u0442\u044c"}]}
|
text2text-generation
|
cointegrated/rut5-small-normalizer
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"t5",
"text2text-generation",
"normalization",
"denoising autoencoder",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #jax #safetensors #t5 #text2text-generation #normalization #denoising autoencoder #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences.
This model was produced by fine-tuning the rut5-small model on the task of reconstructing a sentence:
* restoring word positions (after slightly shuffling them)
* restoring dropped words and punctuation marks (after dropping some of them randomly)
* restoring inflection of words (after changing their inflection randomly using natasha and pymorphy2 packages)
The fine-tuning was performed on a Leipzig web corpus of Russian sentences.
The model can be applied as follows:
A possible output is:
|
[] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #normalization #denoising autoencoder #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
76
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #t5 #text2text-generation #normalization #denoising autoencoder #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model.
It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks.
This model was created by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5small-ruparaphraser) model and stripping 96% of its vocabulary which is unrelated to the Russian language or infrequent.
* The original model has 300M parameters, with 256M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 20K the number of model parameters reduced to 65M parameters, and model size reduced from 1.1GB to 246MB.
* The first 5K tokens in the new vocabulary are taken from the original `mt5-small`.
* The next 15K tokens are the most frequent tokens obtained by tokenizing a Russian web corpus from the [Leipzig corpora collection](https://wortschatz.uni-leipzig.de/en/download/Russian).
The model can be used as follows:
```
# !pip install transformers sentencepiece
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small")
model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small")
text = 'Ехал Грека через реку, видит Грека в реке рак. '
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
hypotheses = model.generate(
**inputs,
do_sample=True, top_p=0.95, num_return_sequences=10,
repetition_penalty=2.5,
max_length=32,
)
for h in hypotheses:
print(tokenizer.decode(h, skip_special_tokens=True))
```
|
{"language": "ru", "license": "mit", "tags": ["paraphrasing", "russian"]}
|
text2text-generation
|
cointegrated/rut5-small
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"mt5",
"text2text-generation",
"paraphrasing",
"russian",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #jax #safetensors #mt5 #text2text-generation #paraphrasing #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a small Russian paraphraser based on the google/mt5-small model.
It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks.
This model was created by taking the alenusch/mt5small-ruparaphraser model and stripping 96% of its vocabulary which is unrelated to the Russian language or infrequent.
* The original model has 300M parameters, with 256M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 20K the number of model parameters reduced to 65M parameters, and model size reduced from 1.1GB to 246MB.
* The first 5K tokens in the new vocabulary are taken from the original 'mt5-small'.
* The next 15K tokens are the most frequent tokens obtained by tokenizing a Russian web corpus from the Leipzig corpora collection.
The model can be used as follows:
|
[] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #mt5 #text2text-generation #paraphrasing #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
71
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #mt5 #text2text-generation #paraphrasing #russian #ru #license-mit #autotrain_compatible #endpoints_compatible #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. -->
# chinese-address-ner
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1080
- Precision: 0.9664
- Recall: 0.9774
- F1: 0.9719
- Accuracy: 0.9758
## Model description
输入一串地址中文信息，比如快递单：`北京市海淀区西北旺东路10号院(马连洼街道西北旺社区东北方向)`，按照行政级别（总有 7 级）抽取地址信息，返回每个 token 的类别。具体类别含义表示如下：
| 返回类别 | BIO 体系 | 解释 |
| ----------- | -------- | ---------------------- |
| **LABEL_0** | O | 忽略信息 |
| **LABEL_1** | B-A1 | 第一级地址（头） |
| **LABEL_2** | I-A1 | 第一级地址（其余部分） |
| ... | ... | ... |
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: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.5055 | 1.0 | 7 | 1.6719 | 0.1977 | 0.2604 | 0.2248 | 0.5649 |
| 1.837 | 2.0 | 14 | 1.0719 | 0.4676 | 0.6 | 0.5256 | 0.7421 |
| 1.0661 | 3.0 | 21 | 0.7306 | 0.6266 | 0.7472 | 0.6816 | 0.8106 |
| 0.8373 | 4.0 | 28 | 0.5197 | 0.6456 | 0.8113 | 0.7191 | 0.8614 |
| 0.522 | 5.0 | 35 | 0.3830 | 0.7667 | 0.8679 | 0.8142 | 0.9001 |
| 0.4295 | 6.0 | 42 | 0.3104 | 0.8138 | 0.8906 | 0.8505 | 0.9178 |
| 0.3483 | 7.0 | 49 | 0.2453 | 0.8462 | 0.9132 | 0.8784 | 0.9404 |
| 0.2471 | 8.0 | 56 | 0.2081 | 0.8403 | 0.9132 | 0.8752 | 0.9428 |
| 0.2299 | 9.0 | 63 | 0.1979 | 0.8419 | 0.9245 | 0.8813 | 0.9420 |
| 0.1761 | 10.0 | 70 | 0.1823 | 0.8830 | 0.9396 | 0.9104 | 0.9500 |
| 0.1434 | 11.0 | 77 | 0.1480 | 0.9036 | 0.9547 | 0.9284 | 0.9629 |
| 0.134 | 12.0 | 84 | 0.1341 | 0.9173 | 0.9623 | 0.9392 | 0.9678 |
| 0.128 | 13.0 | 91 | 0.1365 | 0.9375 | 0.9623 | 0.9497 | 0.9694 |
| 0.0824 | 14.0 | 98 | 0.1159 | 0.9557 | 0.9774 | 0.9664 | 0.9734 |
| 0.0744 | 15.0 | 105 | 0.1092 | 0.9591 | 0.9736 | 0.9663 | 0.9766 |
| 0.0569 | 16.0 | 112 | 0.1117 | 0.9556 | 0.9736 | 0.9645 | 0.9742 |
| 0.0559 | 17.0 | 119 | 0.1040 | 0.9628 | 0.9774 | 0.9700 | 0.9790 |
| 0.0456 | 18.0 | 126 | 0.1052 | 0.9593 | 0.9774 | 0.9682 | 0.9782 |
| 0.0405 | 19.0 | 133 | 0.1133 | 0.9590 | 0.9698 | 0.9644 | 0.9718 |
| 0.0315 | 20.0 | 140 | 0.1060 | 0.9591 | 0.9736 | 0.9663 | 0.9750 |
| 0.0262 | 21.0 | 147 | 0.1087 | 0.9554 | 0.9698 | 0.9625 | 0.9718 |
| 0.0338 | 22.0 | 154 | 0.1183 | 0.9625 | 0.9698 | 0.9662 | 0.9726 |
| 0.0225 | 23.0 | 161 | 0.1080 | 0.9664 | 0.9774 | 0.9719 | 0.9758 |
| 0.028 | 24.0 | 168 | 0.1057 | 0.9591 | 0.9736 | 0.9663 | 0.9742 |
| 0.0202 | 25.0 | 175 | 0.1062 | 0.9628 | 0.9774 | 0.9700 | 0.9766 |
| 0.0168 | 26.0 | 182 | 0.1097 | 0.9664 | 0.9774 | 0.9719 | 0.9758 |
| 0.0173 | 27.0 | 189 | 0.1093 | 0.9628 | 0.9774 | 0.9700 | 0.9774 |
| 0.0151 | 28.0 | 196 | 0.1162 | 0.9628 | 0.9774 | 0.9700 | 0.9766 |
| 0.0135 | 29.0 | 203 | 0.1126 | 0.9483 | 0.9698 | 0.9590 | 0.9758 |
| 0.0179 | 30.0 | 210 | 0.1100 | 0.9449 | 0.9698 | 0.9572 | 0.9774 |
| 0.0161 | 31.0 | 217 | 0.1098 | 0.9449 | 0.9698 | 0.9572 | 0.9766 |
| 0.0158 | 32.0 | 224 | 0.1191 | 0.9483 | 0.9698 | 0.9590 | 0.9734 |
| 0.0151 | 33.0 | 231 | 0.1058 | 0.9483 | 0.9698 | 0.9590 | 0.9750 |
| 0.0121 | 34.0 | 238 | 0.0990 | 0.9593 | 0.9774 | 0.9682 | 0.9790 |
| 0.0092 | 35.0 | 245 | 0.1128 | 0.9519 | 0.9698 | 0.9607 | 0.9774 |
| 0.0097 | 36.0 | 252 | 0.1181 | 0.9627 | 0.9736 | 0.9681 | 0.9766 |
| 0.0118 | 37.0 | 259 | 0.1185 | 0.9591 | 0.9736 | 0.9663 | 0.9782 |
| 0.0118 | 38.0 | 266 | 0.1021 | 0.9557 | 0.9774 | 0.9664 | 0.9823 |
| 0.0099 | 39.0 | 273 | 0.1000 | 0.9559 | 0.9811 | 0.9683 | 0.9815 |
| 0.0102 | 40.0 | 280 | 0.1025 | 0.9559 | 0.9811 | 0.9683 | 0.9815 |
| 0.0068 | 41.0 | 287 | 0.1080 | 0.9522 | 0.9774 | 0.9646 | 0.9807 |
| 0.0105 | 42.0 | 294 | 0.1157 | 0.9449 | 0.9698 | 0.9572 | 0.9766 |
| 0.0083 | 43.0 | 301 | 0.1207 | 0.9380 | 0.9698 | 0.9536 | 0.9766 |
| 0.0077 | 44.0 | 308 | 0.1208 | 0.9483 | 0.9698 | 0.9590 | 0.9766 |
| 0.0077 | 45.0 | 315 | 0.1176 | 0.9483 | 0.9698 | 0.9590 | 0.9774 |
| 0.0071 | 46.0 | 322 | 0.1137 | 0.9483 | 0.9698 | 0.9590 | 0.9790 |
| 0.0075 | 47.0 | 329 | 0.1144 | 0.9483 | 0.9698 | 0.9590 | 0.9782 |
| 0.0084 | 48.0 | 336 | 0.1198 | 0.9483 | 0.9698 | 0.9590 | 0.9766 |
| 0.0103 | 49.0 | 343 | 0.1217 | 0.9519 | 0.9698 | 0.9607 | 0.9766 |
| 0.0087 | 50.0 | 350 | 0.1230 | 0.9519 | 0.9698 | 0.9607 | 0.9766 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.0
- Datasets 1.9.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "chinese-address-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.975825946817083}}]}]}
|
token-classification
|
jiaqianjing/chinese-address-ner
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
chinese-address-ner
===================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1080
* Precision: 0.9664
* Recall: 0.9774
* F1: 0.9719
* Accuracy: 0.9758
Model description
-----------------
输入一串地址中文信息，比如快递单：'北京市海淀区西北旺东路10号院(马连洼街道西北旺社区东北方向)'，按照行政级别（总有 7 级）抽取地址信息，返回每个 token 的类别。具体类别含义表示如下：
返回类别: LABEL\_0, BIO 体系: O, 解释: 忽略信息
返回类别: LABEL\_1, BIO 体系: B-A1, 解释: 第一级地址（头）
返回类别: LABEL\_2, BIO 体系: I-A1, 解释: 第一级地址（其余部分）
返回类别: ..., BIO 体系: ..., 解释: ...
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: 50
* eval\_batch\_size: 50
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.0
* Datasets 1.9.0
* 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: 50\n* eval\\_batch\\_size: 50\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.0\n* Datasets 1.9.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #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: 50\n* eval\\_batch\\_size: 50\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.0\n* Datasets 1.9.0\n* Tokenizers 0.10.3"
] |
[
52,
98,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #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: 50\n* eval\\_batch\\_size: 50\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.0\n* Datasets 1.9.0\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. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2500
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0975 | 1.0 | 291 | 1.7060 |
| 1.648 | 2.0 | 582 | 1.4280 |
| 1.4837 | 3.0 | 873 | 1.3980 |
| 1.3978 | 4.0 | 1164 | 1.4040 |
| 1.3314 | 5.0 | 1455 | 1.2032 |
| 1.2954 | 6.0 | 1746 | 1.2814 |
| 1.2448 | 7.0 | 2037 | 1.2635 |
| 1.1983 | 8.0 | 2328 | 1.2071 |
| 1.1849 | 9.0 | 2619 | 1.1675 |
| 1.1414 | 10.0 | 2910 | 1.2095 |
| 1.1314 | 11.0 | 3201 | 1.1858 |
| 1.0943 | 12.0 | 3492 | 1.1658 |
| 1.0838 | 13.0 | 3783 | 1.2336 |
| 1.0733 | 14.0 | 4074 | 1.1606 |
| 1.0627 | 15.0 | 4365 | 1.1188 |
| 1.055 | 16.0 | 4656 | 1.2500 |
### 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"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]}
|
fill-mask
|
coldfir3/bert-base-uncased-issues-128
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2500
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: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16
### 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: 5e-05\n* train\\_batch\\_size: 32\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: 16",
"### 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 #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #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: 5e-05\n* train\\_batch\\_size: 32\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: 16",
"### 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"
] |
[
63,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #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: 5e-05\n* train\\_batch\\_size: 32\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: 16### 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
|
<!-- 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: 0.2175
- Accuracy: 0.922
- F1: 0.9222
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8262 | 1.0 | 250 | 0.3073 | 0.904 | 0.9021 |
| 0.2484 | 2.0 | 500 | 0.2175 | 0.922 | 0.9222 |
### 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"], "base_model": "distilbert-base-uncased", "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": 0.922, "name": "Accuracy"}, {"type": "f1", "value": 0.9222116474112371, "name": "F1"}]}]}]}
|
text-classification
|
coldfir3/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"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 #base_model-distilbert-base-uncased #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: 0.2175
* Accuracy: 0.922
* F1: 0.9222
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 #base_model-distilbert-base-uncased #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"
] |
[
81,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #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
|
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1759
- F1: 0.8527
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3038 | 1.0 | 835 | 0.1922 | 0.8065 |
| 0.1559 | 2.0 | 1670 | 0.1714 | 0.8422 |
| 0.1002 | 3.0 | 2505 | 0.1759 | 0.8527 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]}
|
token-classification
|
coldfir3/xlm-roberta-base-finetuned-panx-all
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1759
* F1: 0.8527
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: 24
* eval\_batch\_size: 24
* 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 4.15.0
* 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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
65,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1667
- F1: 0.8582
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2885 | 1.0 | 715 | 0.1817 | 0.8287 |
| 0.1497 | 2.0 | 1430 | 0.1618 | 0.8442 |
| 0.0944 | 3.0 | 2145 | 0.1667 | 0.8582 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]}
|
token-classification
|
coldfir3/xlm-roberta-base-finetuned-panx-de-fr
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* F1: 0.8582
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: 24
* eval\_batch\_size: 24
* 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 4.15.0
* 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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
65,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3925
- F1: 0.7075
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1493 | 1.0 | 50 | 0.5884 | 0.4748 |
| 0.5135 | 2.0 | 100 | 0.4088 | 0.6623 |
| 0.3558 | 3.0 | 150 | 0.3925 | 0.7075 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "metrics": [{"type": "f1", "value": 0.7075365579302588, "name": "F1"}]}]}]}
|
token-classification
|
coldfir3/xlm-roberta-base-finetuned-panx-en
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3925
* F1: 0.7075
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: 24
* eval\_batch\_size: 24
* 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 4.15.0
* 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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
64,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2651
- F1: 0.8355
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5954 | 1.0 | 191 | 0.3346 | 0.7975 |
| 0.2689 | 2.0 | 382 | 0.2900 | 0.8347 |
| 0.1821 | 3.0 | 573 | 0.2651 | 0.8355 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "metrics": [{"type": "f1", "value": 0.8354854938789199, "name": "F1"}]}]}]}
|
token-classification
|
coldfir3/xlm-roberta-base-finetuned-panx-fr
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2651
* F1: 0.8355
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: 24
* eval\_batch\_size: 24
* 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 4.15.0
* 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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
76,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2323
- F1: 0.8228
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8126 | 1.0 | 70 | 0.3361 | 0.7231 |
| 0.2995 | 2.0 | 140 | 0.2526 | 0.8079 |
| 0.1865 | 3.0 | 210 | 0.2323 | 0.8228 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "metrics": [{"type": "f1", "value": 0.822805578342904, "name": "F1"}]}]}]}
|
token-classification
|
coldfir3/xlm-roberta-base-finetuned-panx-it
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2323
* F1: 0.8228
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: 24
* eval\_batch\_size: 24
* 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 4.15.0
* 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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
76,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #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: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
colochoplay/DialoGTP-small-harrypotter
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model
|
[
"# Harry Potter DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
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null | null |
transformers
|
# BERT base Japanese model
This repository contains a BERT base model trained on Japanese Wikipedia dataset.
## Training data
[Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of June 20, 2021 which is released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) is used for training.
The dataset is splitted into three subsets - train, valid and test. Both tokenizer and model are trained with the train split.
## Model description
The model architecture is the same as BERT base model (hidden_size: 768, num_hidden_layers: 12, num_attention_heads: 12, max_position_embeddings: 512) except for a vocabulary size.
The vocabulary size is set to 32,000 instead of an original size of 30,522.
For the model, `transformers.BertForPreTraining` is used.
## Tokenizer description
[SentencePiece](https://github.com/google/sentencepiece) tokenizer is used as a tokenizer for this model.
While training, the tokenizer model was trained with 1,000,000 samples which were extracted from the train split.
The vocabulary size is set to 32,000. A `add_dummy_prefix` option is set to `True` because words are not separated by whitespaces in Japanese.
After training, the model is imported to `transformers.DebertaV2Tokenizer` because it supports SentencePiece models and its behavior is consistent when `use_fast` option is set to `True` or `False`.
**Note:**
The meaning of "consistent" here is as follows.
For example, AlbertTokenizer provides AlbertTokenizer and AlbertTokenizerFast. Fast model is used as default. However, the tokenization behavior between them is different and a behavior this mdoel expects is the verions of not fast.
Although `use_fast=False` option passing to AutoTokenier or pipeline solves this problem to force to use not fast version of the tokenizer, this option cannot be passed to config.json or model card.
Therefore unexpected behavior happens when using Inference API. To avoid this kind of problems, `transformers.DebertaV2Tokenizer` is used in this model.
## Training
Training details are as follows.
* gradient update is every 256 samples (batch size: 8, accumulate_grad_batches: 32)
* gradient clip norm is 1.0
* Learning rate starts from 0 and linearly increased to 0.0001 in the first 10,000 steps
* The training set contains around 20M samples. Because 80k * 256 ~ 20M, 1 epochs has around 80k steps.
Trainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.
The training continued until validation loss got worse. Totally the number of training steps were around 214k.
The test set loss was 2.80 .
Training code is available in [a GitHub repository](https://github.com/colorfulscoop/bert-ja).
## Usage
First, install dependecies.
```sh
$ pip install torch==1.8.0 transformers==4.8.2 sentencepiece==0.1.95
```
Then use `transformers.pipeline` to try mask fill task.
```sh
>>> import transformers
>>> pipeline = transformers.pipeline("fill-mask", "colorfulscoop/bert-base-ja", revision="v1.0")
>>> pipeline("専門として[MASK]を専攻しています")
[{'sequence': '専門として工学を専攻しています', 'score': 0.03630176931619644, 'token': 3988, 'token_str': '工学'}, {'sequence': '専門として政治学を専攻しています', 'score': 0.03547220677137375, 'token': 22307, 'token_str': '政治学'}, {'sequence': '専門として教育を専攻しています', 'score': 0.03162326663732529, 'token': 414, 'token_str': '教育'}, {'sequence': '専門として経済学を専攻しています', 'score': 0.026036914438009262, 'token': 6814, 'token_str': '経済学'}, {'sequence': '専門として法学を専攻しています', 'score': 0.02561848610639572, 'token': 10810, 'token_str': '法学'}]
```
Note: specifying a `revision` option is recommended to keep reproducibility when downloading a model via `transformers.pipeline` or `transformers.AutoModel.from_pretrained` .
## License
Copyright (c) 2021 Colorful Scoop
All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
**Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
---
This model utilizes the following data as training data
* **Name:** ウィキペディア (Wikipedia): フリー百科事典
* **Credit:** https://ja.wikipedia.org/
* **License:** [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/)
* **Link:** https://ja.wikipedia.org/
|
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": "wikipedia", "pipeline_tag": "fill-mask", "widget": [{"text": "\u5f97\u610f\u306a\u79d1\u76ee\u306f[MASK]\u3067\u3059\u3002"}]}
|
fill-mask
|
colorfulscoop/bert-base-ja
|
[
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #tf #bert #pretraining #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# BERT base Japanese model
This repository contains a BERT base model trained on Japanese Wikipedia dataset.
## Training data
Japanese Wikipedia dataset as of June 20, 2021 which is released under Creative Commons Attribution-ShareAlike 3.0 is used for training.
The dataset is splitted into three subsets - train, valid and test. Both tokenizer and model are trained with the train split.
## Model description
The model architecture is the same as BERT base model (hidden_size: 768, num_hidden_layers: 12, num_attention_heads: 12, max_position_embeddings: 512) except for a vocabulary size.
The vocabulary size is set to 32,000 instead of an original size of 30,522.
For the model, 'transformers.BertForPreTraining' is used.
## Tokenizer description
SentencePiece tokenizer is used as a tokenizer for this model.
While training, the tokenizer model was trained with 1,000,000 samples which were extracted from the train split.
The vocabulary size is set to 32,000. A 'add_dummy_prefix' option is set to 'True' because words are not separated by whitespaces in Japanese.
After training, the model is imported to 'transformers.DebertaV2Tokenizer' because it supports SentencePiece models and its behavior is consistent when 'use_fast' option is set to 'True' or 'False'.
Note:
The meaning of "consistent" here is as follows.
For example, AlbertTokenizer provides AlbertTokenizer and AlbertTokenizerFast. Fast model is used as default. However, the tokenization behavior between them is different and a behavior this mdoel expects is the verions of not fast.
Although 'use_fast=False' option passing to AutoTokenier or pipeline solves this problem to force to use not fast version of the tokenizer, this option cannot be passed to URL or model card.
Therefore unexpected behavior happens when using Inference API. To avoid this kind of problems, 'transformers.DebertaV2Tokenizer' is used in this model.
## Training
Training details are as follows.
* gradient update is every 256 samples (batch size: 8, accumulate_grad_batches: 32)
* gradient clip norm is 1.0
* Learning rate starts from 0 and linearly increased to 0.0001 in the first 10,000 steps
* The training set contains around 20M samples. Because 80k * 256 ~ 20M, 1 epochs has around 80k steps.
Trainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.
The training continued until validation loss got worse. Totally the number of training steps were around 214k.
The test set loss was 2.80 .
Training code is available in a GitHub repository.
## Usage
First, install dependecies.
Then use 'transformers.pipeline' to try mask fill task.
Note: specifying a 'revision' option is recommended to keep reproducibility when downloading a model via 'transformers.pipeline' or 'transformers.AutoModel.from_pretrained' .
## License
Copyright (c) 2021 Colorful Scoop
All the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 3.0.
Disclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
---
This model utilizes the following data as training data
* Name: ウィキペディア (Wikipedia): フリー百科事典
* Credit: URL
* License: Creative Commons Attribution-ShareAlike 3.0
* Link: URL
|
[
"# BERT base Japanese model\n\nThis repository contains a BERT base model trained on Japanese Wikipedia dataset.",
"## Training data\n\nJapanese Wikipedia dataset as of June 20, 2021 which is released under Creative Commons Attribution-ShareAlike 3.0 is used for training.\nThe dataset is splitted into three subsets - train, valid and test. Both tokenizer and model are trained with the train split.",
"## Model description\n\nThe model architecture is the same as BERT base model (hidden_size: 768, num_hidden_layers: 12, num_attention_heads: 12, max_position_embeddings: 512) except for a vocabulary size.\nThe vocabulary size is set to 32,000 instead of an original size of 30,522.\n\nFor the model, 'transformers.BertForPreTraining' is used.",
"## Tokenizer description\n\nSentencePiece tokenizer is used as a tokenizer for this model.\n\nWhile training, the tokenizer model was trained with 1,000,000 samples which were extracted from the train split.\nThe vocabulary size is set to 32,000. A 'add_dummy_prefix' option is set to 'True' because words are not separated by whitespaces in Japanese.\n\nAfter training, the model is imported to 'transformers.DebertaV2Tokenizer' because it supports SentencePiece models and its behavior is consistent when 'use_fast' option is set to 'True' or 'False'.\n\nNote:\nThe meaning of \"consistent\" here is as follows.\nFor example, AlbertTokenizer provides AlbertTokenizer and AlbertTokenizerFast. Fast model is used as default. However, the tokenization behavior between them is different and a behavior this mdoel expects is the verions of not fast.\nAlthough 'use_fast=False' option passing to AutoTokenier or pipeline solves this problem to force to use not fast version of the tokenizer, this option cannot be passed to URL or model card.\nTherefore unexpected behavior happens when using Inference API. To avoid this kind of problems, 'transformers.DebertaV2Tokenizer' is used in this model.",
"## Training\n\nTraining details are as follows.\n\n* gradient update is every 256 samples (batch size: 8, accumulate_grad_batches: 32)\n* gradient clip norm is 1.0\n* Learning rate starts from 0 and linearly increased to 0.0001 in the first 10,000 steps\n* The training set contains around 20M samples. Because 80k * 256 ~ 20M, 1 epochs has around 80k steps.\n\nTrainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.\n\nThe training continued until validation loss got worse. Totally the number of training steps were around 214k.\nThe test set loss was 2.80 .\n\nTraining code is available in a GitHub repository.",
"## Usage\n\nFirst, install dependecies.\n\n\n\nThen use 'transformers.pipeline' to try mask fill task.\n\n\n\nNote: specifying a 'revision' option is recommended to keep reproducibility when downloading a model via 'transformers.pipeline' or 'transformers.AutoModel.from_pretrained' .",
"## License\n\nCopyright (c) 2021 Colorful Scoop\n\nAll the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 3.0.\n\nDisclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.\n\n---\n\nThis model utilizes the following data as training data\n\n* Name: ウィキペディア (Wikipedia): フリー百科事典\n* Credit: URL\n* License: Creative Commons Attribution-ShareAlike 3.0\n* Link: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# BERT base Japanese model\n\nThis repository contains a BERT base model trained on Japanese Wikipedia dataset.",
"## Training data\n\nJapanese Wikipedia dataset as of June 20, 2021 which is released under Creative Commons Attribution-ShareAlike 3.0 is used for training.\nThe dataset is splitted into three subsets - train, valid and test. Both tokenizer and model are trained with the train split.",
"## Model description\n\nThe model architecture is the same as BERT base model (hidden_size: 768, num_hidden_layers: 12, num_attention_heads: 12, max_position_embeddings: 512) except for a vocabulary size.\nThe vocabulary size is set to 32,000 instead of an original size of 30,522.\n\nFor the model, 'transformers.BertForPreTraining' is used.",
"## Tokenizer description\n\nSentencePiece tokenizer is used as a tokenizer for this model.\n\nWhile training, the tokenizer model was trained with 1,000,000 samples which were extracted from the train split.\nThe vocabulary size is set to 32,000. A 'add_dummy_prefix' option is set to 'True' because words are not separated by whitespaces in Japanese.\n\nAfter training, the model is imported to 'transformers.DebertaV2Tokenizer' because it supports SentencePiece models and its behavior is consistent when 'use_fast' option is set to 'True' or 'False'.\n\nNote:\nThe meaning of \"consistent\" here is as follows.\nFor example, AlbertTokenizer provides AlbertTokenizer and AlbertTokenizerFast. Fast model is used as default. However, the tokenization behavior between them is different and a behavior this mdoel expects is the verions of not fast.\nAlthough 'use_fast=False' option passing to AutoTokenier or pipeline solves this problem to force to use not fast version of the tokenizer, this option cannot be passed to URL or model card.\nTherefore unexpected behavior happens when using Inference API. To avoid this kind of problems, 'transformers.DebertaV2Tokenizer' is used in this model.",
"## Training\n\nTraining details are as follows.\n\n* gradient update is every 256 samples (batch size: 8, accumulate_grad_batches: 32)\n* gradient clip norm is 1.0\n* Learning rate starts from 0 and linearly increased to 0.0001 in the first 10,000 steps\n* The training set contains around 20M samples. Because 80k * 256 ~ 20M, 1 epochs has around 80k steps.\n\nTrainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.\n\nThe training continued until validation loss got worse. Totally the number of training steps were around 214k.\nThe test set loss was 2.80 .\n\nTraining code is available in a GitHub repository.",
"## Usage\n\nFirst, install dependecies.\n\n\n\nThen use 'transformers.pipeline' to try mask fill task.\n\n\n\nNote: specifying a 'revision' option is recommended to keep reproducibility when downloading a model via 'transformers.pipeline' or 'transformers.AutoModel.from_pretrained' .",
"## License\n\nCopyright (c) 2021 Colorful Scoop\n\nAll the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 3.0.\n\nDisclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.\n\n---\n\nThis model utilizes the following data as training data\n\n* Name: ウィキペディア (Wikipedia): フリー百科事典\n* Credit: URL\n* License: Creative Commons Attribution-ShareAlike 3.0\n* Link: URL"
] |
[
52,
25,
60,
98,
304,
157,
72,
160
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n# BERT base Japanese model\n\nThis repository contains a BERT base model trained on Japanese Wikipedia dataset.## Training data\n\nJapanese Wikipedia dataset as of June 20, 2021 which is released under Creative Commons Attribution-ShareAlike 3.0 is used for training.\nThe dataset is splitted into three subsets - train, valid and test. Both tokenizer and model are trained with the train split.## Model description\n\nThe model architecture is the same as BERT base model (hidden_size: 768, num_hidden_layers: 12, num_attention_heads: 12, max_position_embeddings: 512) except for a vocabulary size.\nThe vocabulary size is set to 32,000 instead of an original size of 30,522.\n\nFor the model, 'transformers.BertForPreTraining' is used."
] |
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null | null |
transformers
|
# GPT-2 small Japanese model
This repository contains a GPT2-small model trained on Japanese Wikipedia dataset.
## Training data
[Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset as of Aug20, 2021 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) is used for both tokenizer and GPT-2 model.
We splitted the dataset into three subsets - train, valid and test sets. Both tokenizer and model were trained on the train set.
Train set contains around 540M tokens.
## Model description
The model architecture is the same as GPT-2 small model (n_ctx: 1024, n_embd 768, n_head: 12, n_layer: 12) except for a vocabulary size.
The vocabulary size is set to 32,000 instead of an original size of 50,257.
`transformers.GPT2LMHeadModel` is used for training.
## Tokenizer description
[SentencePiece](https://github.com/google/sentencepiece) is used as a tokenizer for this model.
We utilized 1,000,000 sentences from train set.
The vocabulary size was 32,000.
A `add_dummy_prefix` option was set to `True` because Japanese words are not separated by whitespaces.
After training, the tokenizer model was imported as `transformers.BERTGenerationTokenizer`
because it supports SentencePiece models and it does not add any special tokens as default,
which is useful expecially for a text generation task.
## Training
The model was trained on the train set for 30 epochs with batch size 32. Each sample contained 1024 tokens.
We utilized Adam optimizer. Learning rate was linearly increased from `0` to `1e-4` during the first 10,000 steps.
A clip norm was set to `1.0`.
Test set perplexity of the trained model was 29.13.
Please refer to [GitHub](https://github.com/colorfulscoop/gpt-ja) for more training details.
## Usage
First, install dependecies.
```sh
$ pip install transformers==4.10.0 torch==1.8.1 sentencepiece==0.1.96
```
Then use pipeline to generate sentences.
```sh
>>> import transformers
>>> pipeline = transformers.pipeline("text-generation", "colorfulscoop/gpt2-small-ja")
>>> pipeline("統計的機械学習でのニューラルネットワーク", do_sample=True, top_p=0.95, top_k=50, num_return_sequences=3)
```
**Note:** The default model configuration `config.json` sets parameters for text generation with `do_sample=True`, `top_k=50`, `top_p=0.95`.
Please set these parameters when you need to use different parameters.
## Versions
We recommend to specify `revision` to load the model for reproducibility.
| Revision | Date of Wikipedia dump |
| --- | --- |
| 20210820.1.0 | Aug 20, 2021 |
| 20210301.1.0 | March 1, 2021 |
You can specify `revision` as follows.
```py
# Example of pipeline
>>> transformers.pipeline("text-generation", "colorfulscoop/gpt2-small-ja", revision="20210820.1.0")
# Example of AutoModel
>>> transformers.AutoModel.from_pretrained("colorfulscoop/gpt2-small-ja", revision="20210820.1.0")
```
## License
All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
**Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
**Author:** Colorful Scoop
|
{"language": "ja", "license": "cc", "datasets": "wikipedia", "widget": [{"text": "\u7d71\u8a08\u7684\u6a5f\u68b0\u5b66\u7fd2\u3067\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af"}]}
|
text-generation
|
colorfulscoop/gpt2-small-ja
|
[
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #tf #gpt2 #text-generation #ja #dataset-wikipedia #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
GPT-2 small Japanese model
==========================
This repository contains a GPT2-small model trained on Japanese Wikipedia dataset.
Training data
-------------
Japanese Wikipedia dataset as of Aug20, 2021 released under Creative Commons Attribution-ShareAlike 3.0 is used for both tokenizer and GPT-2 model.
We splitted the dataset into three subsets - train, valid and test sets. Both tokenizer and model were trained on the train set.
Train set contains around 540M tokens.
Model description
-----------------
The model architecture is the same as GPT-2 small model (n\_ctx: 1024, n\_embd 768, n\_head: 12, n\_layer: 12) except for a vocabulary size.
The vocabulary size is set to 32,000 instead of an original size of 50,257.
'transformers.GPT2LMHeadModel' is used for training.
Tokenizer description
---------------------
SentencePiece is used as a tokenizer for this model.
We utilized 1,000,000 sentences from train set.
The vocabulary size was 32,000.
A 'add\_dummy\_prefix' option was set to 'True' because Japanese words are not separated by whitespaces.
After training, the tokenizer model was imported as 'transformers.BERTGenerationTokenizer'
because it supports SentencePiece models and it does not add any special tokens as default,
which is useful expecially for a text generation task.
Training
--------
The model was trained on the train set for 30 epochs with batch size 32. Each sample contained 1024 tokens.
We utilized Adam optimizer. Learning rate was linearly increased from '0' to '1e-4' during the first 10,000 steps.
A clip norm was set to '1.0'.
Test set perplexity of the trained model was 29.13.
Please refer to GitHub for more training details.
Usage
-----
First, install dependecies.
Then use pipeline to generate sentences.
Note: The default model configuration 'URL' sets parameters for text generation with 'do\_sample=True', 'top\_k=50', 'top\_p=0.95'.
Please set these parameters when you need to use different parameters.
Versions
--------
We recommend to specify 'revision' to load the model for reproducibility.
You can specify 'revision' as follows.
License
-------
All the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 3.0.
Disclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
Author: Colorful Scoop
|
[] |
[
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #ja #dataset-wikipedia #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
66
] |
[
"passage: TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #ja #dataset-wikipedia #license-cc #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
sentence-transformers
|
# Sentence BERT base Japanese model
This repository contains a Sentence BERT base model for Japanese.
## Pretrained model
This model utilizes a Japanese BERT model [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) v1.0 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) as a pretrained model.
## Training data
[Japanese SNLI dataset](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) released under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/) is used for training.
Original training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared.
* Train data: 523,005 samples
* Valid data: 10,000 samples
* Test data: 3,916 samples
## Model description
This model utilizes `SentenceTransformer` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) .
The model detail is as below.
```py
>>> from sentence_transformers import SentenceTransformer
>>> SentenceTransformer("colorfulscoop/sbert-base-ja")
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})
)
```
## Training
This model finetuned [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8.
Note: in a original paper of [Sentence BERT](https://arxiv.org/abs/1908.10084), a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16.
Trainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.
After training, test set accuracy reached to 0.8529.
Training code is available in [a GitHub repository](https://github.com/colorfulscoop/sbert-ja).
## Usage
First, install dependecies.
```sh
$ pip install sentence-transformers==2.0.0
```
Then initialize `SentenceTransformer` model and use `encode` method to convert to vectors.
```py
>>> from sentence_transformers import SentenceTransformer
>>> model = SentenceTransformer("colorfulscoop/sbert-base-ja")
>>> sentences = ["外をランニングするのが好きです", "海外旅行に行くのが趣味です"]
>>> model.encode(sentences)
```
## License
Copyright (c) 2021 Colorful Scoop
All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
**Disclaimer:** Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
---
This model utilizes the folllowing pretrained model.
* **Name:** bert-base-ja
* **Credit:** (c) 2021 Colorful Scoop
* **License:** [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/)
* **Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
* **Link:** https://huggingface.co/colorfulscoop/bert-base-ja
---
This model utilizes the following data for fine-tuning.
* **Name:** 日本語SNLI(JSNLI)データセット
* **Credit:** [https://nlp.ist.i.kyoto-u.ac.jp/index.php?日本語SNLI(JSNLI)データセット](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88)
* **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
* **Link:** [https://nlp.ist.i.kyoto-u.ac.jp/index.php?日本語SNLI(JSNLI)データセット](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88)
|
{"language": "ja", "license": "cc-by-sa-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "\u8d70\u308b\u306e\u304c\u8da3\u5473\u3067\u3059", "sentences": ["\u5916\u3092\u30e9\u30f3\u30cb\u30f3\u30b0\u3059\u308b\u306e\u304c\u597d\u304d\u3067\u3059", "\u904b\u52d5\u306f\u305d\u3053\u305d\u3053\u3067\u3059", "\u8d70\u308b\u306e\u306f\u5acc\u3044\u3067\u3059"]}}
|
sentence-similarity
|
colorfulscoop/sbert-base-ja
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"ja",
"arxiv:1908.10084",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1908.10084"
] |
[
"ja"
] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #ja #arxiv-1908.10084 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Sentence BERT base Japanese model
This repository contains a Sentence BERT base model for Japanese.
## Pretrained model
This model utilizes a Japanese BERT model colorfulscoop/bert-base-ja v1.0 released under Creative Commons Attribution-ShareAlike 3.0 as a pretrained model.
## Training data
Japanese SNLI dataset released under Creative Commons Attribution-ShareAlike 4.0 is used for training.
Original training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared.
* Train data: 523,005 samples
* Valid data: 10,000 samples
* Test data: 3,916 samples
## Model description
This model utilizes 'SentenceTransformer' model from the sentence-transformers .
The model detail is as below.
## Training
This model finetuned colorfulscoop/bert-base-ja with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8.
Note: in a original paper of Sentence BERT, a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16.
Trainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.
After training, test set accuracy reached to 0.8529.
Training code is available in a GitHub repository.
## Usage
First, install dependecies.
Then initialize 'SentenceTransformer' model and use 'encode' method to convert to vectors.
## License
Copyright (c) 2021 Colorful Scoop
All the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 4.0.
Disclaimer: Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
---
This model utilizes the folllowing pretrained model.
* Name: bert-base-ja
* Credit: (c) 2021 Colorful Scoop
* License: Creative Commons Attribution-ShareAlike 3.0
* Disclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.
* Link: URL
---
This model utilizes the following data for fine-tuning.
* Name: 日本語SNLI(JSNLI)データセット
* Credit: URL.i.URL?日本語SNLI(JSNLI)データセット
* License: CC BY-SA 4.0
* Link: URL.i.URL?日本語SNLI(JSNLI)データセット
|
[
"# Sentence BERT base Japanese model\n\nThis repository contains a Sentence BERT base model for Japanese.",
"## Pretrained model\n\nThis model utilizes a Japanese BERT model colorfulscoop/bert-base-ja v1.0 released under Creative Commons Attribution-ShareAlike 3.0 as a pretrained model.",
"## Training data\n\nJapanese SNLI dataset released under Creative Commons Attribution-ShareAlike 4.0 is used for training.\n\nOriginal training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared.\n\n* Train data: 523,005 samples\n* Valid data: 10,000 samples\n* Test data: 3,916 samples",
"## Model description\n\nThis model utilizes 'SentenceTransformer' model from the sentence-transformers .\nThe model detail is as below.",
"## Training\n\nThis model finetuned colorfulscoop/bert-base-ja with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8.\n\nNote: in a original paper of Sentence BERT, a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16.\n\nTrainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.\n\nAfter training, test set accuracy reached to 0.8529.\n\nTraining code is available in a GitHub repository.",
"## Usage\n\nFirst, install dependecies.\n\n\n\nThen initialize 'SentenceTransformer' model and use 'encode' method to convert to vectors.",
"## License\n\nCopyright (c) 2021 Colorful Scoop\n\nAll the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 4.0.\n\nDisclaimer: Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.\n\n---\n\nThis model utilizes the folllowing pretrained model.\n\n* Name: bert-base-ja\n* Credit: (c) 2021 Colorful Scoop\n* License: Creative Commons Attribution-ShareAlike 3.0\n* Disclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.\n* Link: URL\n\n---\n\nThis model utilizes the following data for fine-tuning.\n\n* Name: 日本語SNLI(JSNLI)データセット\n* Credit: URL.i.URL?日本語SNLI(JSNLI)データセット\n* License: CC BY-SA 4.0\n* Link: URL.i.URL?日本語SNLI(JSNLI)データセット"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #ja #arxiv-1908.10084 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Sentence BERT base Japanese model\n\nThis repository contains a Sentence BERT base model for Japanese.",
"## Pretrained model\n\nThis model utilizes a Japanese BERT model colorfulscoop/bert-base-ja v1.0 released under Creative Commons Attribution-ShareAlike 3.0 as a pretrained model.",
"## Training data\n\nJapanese SNLI dataset released under Creative Commons Attribution-ShareAlike 4.0 is used for training.\n\nOriginal training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared.\n\n* Train data: 523,005 samples\n* Valid data: 10,000 samples\n* Test data: 3,916 samples",
"## Model description\n\nThis model utilizes 'SentenceTransformer' model from the sentence-transformers .\nThe model detail is as below.",
"## Training\n\nThis model finetuned colorfulscoop/bert-base-ja with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8.\n\nNote: in a original paper of Sentence BERT, a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16.\n\nTrainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.\n\nAfter training, test set accuracy reached to 0.8529.\n\nTraining code is available in a GitHub repository.",
"## Usage\n\nFirst, install dependecies.\n\n\n\nThen initialize 'SentenceTransformer' model and use 'encode' method to convert to vectors.",
"## License\n\nCopyright (c) 2021 Colorful Scoop\n\nAll the models included in this repository are licensed under Creative Commons Attribution-ShareAlike 4.0.\n\nDisclaimer: Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.\n\n---\n\nThis model utilizes the folllowing pretrained model.\n\n* Name: bert-base-ja\n* Credit: (c) 2021 Colorful Scoop\n* License: Creative Commons Attribution-ShareAlike 3.0\n* Disclaimer: The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.\n* Link: URL\n\n---\n\nThis model utilizes the following data for fine-tuning.\n\n* Name: 日本語SNLI(JSNLI)データセット\n* Credit: URL.i.URL?日本語SNLI(JSNLI)データセット\n* License: CC BY-SA 4.0\n* Link: URL.i.URL?日本語SNLI(JSNLI)データセット"
] |
[
64,
24,
41,
72,
30,
186,
33,
290
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #ja #arxiv-1908.10084 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n# Sentence BERT base Japanese model\n\nThis repository contains a Sentence BERT base model for Japanese.## Pretrained model\n\nThis model utilizes a Japanese BERT model colorfulscoop/bert-base-ja v1.0 released under Creative Commons Attribution-ShareAlike 3.0 as a pretrained model.## Training data\n\nJapanese SNLI dataset released under Creative Commons Attribution-ShareAlike 4.0 is used for training.\n\nOriginal training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared.\n\n* Train data: 523,005 samples\n* Valid data: 10,000 samples\n* Test data: 3,916 samples## Model description\n\nThis model utilizes 'SentenceTransformer' model from the sentence-transformers .\nThe model detail is as below.## Training\n\nThis model finetuned colorfulscoop/bert-base-ja with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8.\n\nNote: in a original paper of Sentence BERT, a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16.\n\nTrainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.\n\nAfter training, test set accuracy reached to 0.8529.\n\nTraining code is available in a GitHub repository.## Usage\n\nFirst, install dependecies.\n\n\n\nThen initialize 'SentenceTransformer' model and use 'encode' method to convert to vectors."
] |
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null | null |
transformers
|
# Czech wav2vec2-xls-r-300m-cs-250
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 8.0 dataset as well as other datasets listed below.
It achieves the following results on the evaluation set:
- Loss: 0.1271
- Wer: 0.1475
- Cer: 0.0329
The `eval.py` script results using a LM are:
- WER: 0.07274312090176113
- CER: 0.021207369275558875
## Model description
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
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("mozilla-foundation/common_voice_8_0", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-cs-250 --dataset mozilla-foundation/common-voice_8_0 --split test --config cs
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training, as well as the following datasets:
- Šmídl, Luboš and Pražák, Aleš, 2013, OVM – Otázky Václava Moravce, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11858/00-097C-0000-000D-EC98-3.
- Pražák, Aleš and Šmídl, Luboš, 2012, Czech Parliament Meetings, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11858/00-097C-0000-0005-CF9C-4.
- Plátek, Ondřej; Dušek, Ondřej and Jurčíček, Filip, 2016, Vystadial 2016 – Czech data, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11234/1-1740.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.4203 | 0.16 | 800 | 3.3148 | 1.0 | 1.0 |
| 2.8151 | 0.32 | 1600 | 0.8508 | 0.8938 | 0.2345 |
| 0.9411 | 0.48 | 2400 | 0.3335 | 0.3723 | 0.0847 |
| 0.7408 | 0.64 | 3200 | 0.2573 | 0.2840 | 0.0642 |
| 0.6516 | 0.8 | 4000 | 0.2365 | 0.2581 | 0.0595 |
| 0.6242 | 0.96 | 4800 | 0.2039 | 0.2433 | 0.0541 |
| 0.5754 | 1.12 | 5600 | 0.1832 | 0.2156 | 0.0482 |
| 0.5626 | 1.28 | 6400 | 0.1827 | 0.2091 | 0.0463 |
| 0.5342 | 1.44 | 7200 | 0.1744 | 0.2033 | 0.0468 |
| 0.4965 | 1.6 | 8000 | 0.1705 | 0.1963 | 0.0444 |
| 0.5047 | 1.76 | 8800 | 0.1604 | 0.1889 | 0.0422 |
| 0.4814 | 1.92 | 9600 | 0.1604 | 0.1827 | 0.0411 |
| 0.4471 | 2.09 | 10400 | 0.1566 | 0.1822 | 0.0406 |
| 0.4509 | 2.25 | 11200 | 0.1619 | 0.1853 | 0.0432 |
| 0.4415 | 2.41 | 12000 | 0.1513 | 0.1764 | 0.0397 |
| 0.4313 | 2.57 | 12800 | 0.1515 | 0.1739 | 0.0392 |
| 0.4163 | 2.73 | 13600 | 0.1445 | 0.1695 | 0.0377 |
| 0.4142 | 2.89 | 14400 | 0.1478 | 0.1699 | 0.0385 |
| 0.4184 | 3.05 | 15200 | 0.1430 | 0.1669 | 0.0376 |
| 0.3886 | 3.21 | 16000 | 0.1433 | 0.1644 | 0.0374 |
| 0.3795 | 3.37 | 16800 | 0.1426 | 0.1648 | 0.0373 |
| 0.3859 | 3.53 | 17600 | 0.1357 | 0.1604 | 0.0361 |
| 0.3762 | 3.69 | 18400 | 0.1344 | 0.1558 | 0.0349 |
| 0.384 | 3.85 | 19200 | 0.1379 | 0.1576 | 0.0359 |
| 0.3762 | 4.01 | 20000 | 0.1344 | 0.1539 | 0.0346 |
| 0.3559 | 4.17 | 20800 | 0.1339 | 0.1525 | 0.0351 |
| 0.3683 | 4.33 | 21600 | 0.1315 | 0.1518 | 0.0342 |
| 0.3572 | 4.49 | 22400 | 0.1307 | 0.1507 | 0.0342 |
| 0.3494 | 4.65 | 23200 | 0.1294 | 0.1491 | 0.0335 |
| 0.3476 | 4.81 | 24000 | 0.1287 | 0.1491 | 0.0336 |
| 0.3475 | 4.97 | 24800 | 0.1271 | 0.1475 | 0.0329 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week"], "datasets": ["mozilla-foundation/common_voice_8_0", "ovm", "pscr", "vystadial2016"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "Czech comodoro Wav2Vec2 XLSR 300M 250h data", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "cs"}, "metrics": [{"type": "wer", "value": 7.3, "name": "Test WER"}, {"type": "cer", "value": 2.1, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 43.44, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 38.5, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-cs-250
|
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:ovm",
"dataset:pscr",
"dataset:vystadial2016",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"cs"
] |
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-mozilla-foundation/common_voice_8_0 #dataset-ovm #dataset-pscr #dataset-vystadial2016 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
Czech wav2vec2-xls-r-300m-cs-250
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice 8.0 dataset as well as other datasets listed below.
It achieves the following results on the evaluation set:
* Loss: 0.1271
* Wer: 0.1475
* Cer: 0.0329
The 'URL' script results using a LM are:
* WER: 0.07274312090176113
* CER: 0.021207369275558875
Model description
-----------------
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
The model can be used directly (without a language model) as follows:
Evaluation
----------
The model can be evaluated using the attached 'URL' script:
Training and evaluation data
----------------------------
The Common Voice 8.0 'train' and 'validation' datasets were used for training, as well as the following datasets:
* Šmídl, Luboš and Pražák, Aleš, 2013, OVM – Otázky Václava Moravce, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, URL
* Pražák, Aleš and Šmídl, Luboš, 2012, Czech Parliament Meetings, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, URL
* Plátek, Ondřej; Dušek, Ondřej and Jurčíček, Filip, 2016, Vystadial 2016 – Czech data, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, URL
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 800
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 800\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-mozilla-foundation/common_voice_8_0 #dataset-ovm #dataset-pscr #dataset-vystadial2016 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 800\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
169,
130,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-mozilla-foundation/common_voice_8_0 #dataset-ovm #dataset-pscr #dataset-vystadial2016 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps: 800\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.1+cu102\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-xls-r-300m-cs-cv8
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 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.2327
- Wer: 0.1608
- Cer: 0.0376
The `eval.py` script results using a LM are:
WER: 0.10281503199350225
CER: 0.02622802241689026
## Model description
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
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("mozilla-foundation/common_voice_8_0", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-cv8")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-cv8")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-cs-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config cs
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training
## Training procedure
### Training hyperparameters
The following hyperparameters were used during first stage of training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
- mixed_precision_training: Native AMP
The following hyperparameters were used during second stage of training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 7.2926 | 8.06 | 250 | 3.8497 | 1.0 | 1.0 |
| 3.417 | 16.13 | 500 | 3.2852 | 1.0 | 0.9857 |
| 2.0264 | 24.19 | 750 | 0.7099 | 0.7342 | 0.1768 |
| 0.4018 | 32.25 | 1000 | 0.6188 | 0.6415 | 0.1551 |
| 0.2444 | 40.32 | 1250 | 0.6632 | 0.6362 | 0.1600 |
| 0.1882 | 48.38 | 1500 | 0.6070 | 0.5783 | 0.1388 |
| 0.153 | 56.44 | 1750 | 0.6425 | 0.5720 | 0.1377 |
| 0.1214 | 64.51 | 2000 | 0.6363 | 0.5546 | 0.1337 |
| 0.1011 | 72.57 | 2250 | 0.6310 | 0.5222 | 0.1224 |
| 0.0879 | 80.63 | 2500 | 0.6353 | 0.5258 | 0.1253 |
| 0.0782 | 88.7 | 2750 | 0.6078 | 0.4904 | 0.1127 |
| 0.0709 | 96.76 | 3000 | 0.6465 | 0.4960 | 0.1154 |
| 0.0661 | 104.82 | 3250 | 0.6622 | 0.4945 | 0.1166 |
| 0.0616 | 112.89 | 3500 | 0.6440 | 0.4786 | 0.1104 |
| 0.0579 | 120.95 | 3750 | 0.6815 | 0.4887 | 0.1144 |
| 0.0549 | 129.03 | 4000 | 0.6603 | 0.4780 | 0.1105 |
| 0.0527 | 137.09 | 4250 | 0.6652 | 0.4749 | 0.1090 |
| 0.0506 | 145.16 | 4500 | 0.6958 | 0.4846 | 0.1133 |
Further fine-tuning with slightly different architecture and higher learning rate:
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.576 | 8.06 | 250 | 0.2411 | 0.2340 | 0.0502 |
| 0.2564 | 16.13 | 500 | 0.2305 | 0.2097 | 0.0492 |
| 0.2018 | 24.19 | 750 | 0.2371 | 0.2059 | 0.0494 |
| 0.1549 | 32.25 | 1000 | 0.2298 | 0.1844 | 0.0435 |
| 0.1224 | 40.32 | 1250 | 0.2288 | 0.1725 | 0.0407 |
| 0.1004 | 48.38 | 1500 | 0.2327 | 0.1608 | 0.0376 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
{"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Czech comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "cs"}, "metrics": [{"type": "wer", "value": 10.3, "name": "Test WER"}, {"type": "cer", "value": 2.6, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 54.29, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 44.55, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-cs-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"cs",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"cs"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #cs #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
wav2vec2-xls-r-300m-cs-cv8
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice 8.0 dataset.
It achieves the following results on the evaluation set while training:
* Loss: 0.2327
* Wer: 0.1608
* Cer: 0.0376
The 'URL' script results using a LM are:
WER: 0.10281503199350225
CER: 0.02622802241689026
Model description
-----------------
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
The model can be used directly (without a language model) as follows:
Evaluation
----------
The model can be evaluated using the attached 'URL' script:
Training and evaluation data
----------------------------
The Common Voice 8.0 'train' and 'validation' datasets were used for training
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during first stage of training:
* learning\_rate: 7e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 20
* total\_train\_batch\_size: 640
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 150
* mixed\_precision\_training: Native AMP
The following hyperparameters were used during second stage of training:
* learning\_rate: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 20
* total\_train\_batch\_size: 640
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 50
* mixed\_precision\_training: Native AMP
### Training results
Further fine-tuning with slightly different architecture and higher learning rate:
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during first stage of training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\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: 150\n* mixed\\_precision\\_training: Native AMP\n\n\nThe following hyperparameters were used during second stage of training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\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: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results\n\n\n\nFurther fine-tuning with slightly different architecture and higher learning rate:",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #cs #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during first stage of training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\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: 150\n* mixed\\_precision\\_training: Native AMP\n\n\nThe following hyperparameters were used during second stage of training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\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: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results\n\n\n\nFurther fine-tuning with slightly different architecture and higher learning rate:",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0"
] |
[
122,
316,
20,
41
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #cs #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during first stage of training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\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: 150\n* mixed\\_precision\\_training: Native AMP\n\n\nThe following hyperparameters were used during second stage of training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 20\n* total\\_train\\_batch\\_size: 640\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: 50\n* mixed\\_precision\\_training: Native AMP### Training results\n\n\n\nFurther fine-tuning with slightly different architecture and higher learning rate:### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Czech
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Czech test data of Common Voice 6.1
```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", "cs", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return 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**: 22.20 %
## Training
The Common Voice `train` and `validation` datasets were used for training
# TODO The script used for training can be found [here](...)
|
{"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "Czech comodoro Wav2Vec2 XLSR 300M CV6.1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 6.1", "type": "common_voice", "args": "cs"}, "metrics": [{"type": "wer", "value": 22.2, "name": "Test WER"}, {"type": "cer", "value": 5.1, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 66.78, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 57.52, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-cs
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"cs"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Czech
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.
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:
## Evaluation
The model can be evaluated as follows on the Czech test data of Common Voice 6.1
Test Result: 22.20 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training
# TODO The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-Czech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.\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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Czech test data of Common Voice 6.1 \n\n\n\n\nTest Result: 22.20 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training",
"# TODO The script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Czech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.\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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Czech test data of Common Voice 6.1 \n\n\n\n\nTest Result: 22.20 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training",
"# TODO The script used for training can be found here"
] |
[
102,
65,
20,
27,
22,
12
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #xlsr-fine-tuning-week #cs #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Czech\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset.\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:## Evaluation\n\nThe model can be evaluated as follows on the Czech test data of Common Voice 6.1 \n\n\n\n\nTest Result: 22.20 %## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training# TODO The script used for training can be found here"
] |
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] |
null | null |
transformers
|
# Upper Sorbian wav2vec2-xls-r-300m-hsb-cv8
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.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Wer: 0.5037
- Cer: 0.1278
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-hsb-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config hsb
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 200
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 4.3121 | 19.35 | 1200 | 3.2059 | 1.0 | 1.0 |
| 2.6525 | 38.71 | 2400 | 1.1324 | 0.9387 | 0.3204 |
| 1.3644 | 58.06 | 3600 | 0.8767 | 0.8099 | 0.2271 |
| 1.093 | 77.42 | 4800 | 0.8739 | 0.7603 | 0.2090 |
| 0.9546 | 96.77 | 6000 | 0.8454 | 0.6983 | 0.1882 |
| 0.8554 | 116.13 | 7200 | 0.8197 | 0.6484 | 0.1708 |
| 0.775 | 135.48 | 8400 | 0.8452 | 0.6345 | 0.1681 |
| 0.7167 | 154.84 | 9600 | 0.8551 | 0.6241 | 0.1631 |
| 0.6609 | 174.19 | 10800 | 0.8442 | 0.5821 | 0.1531 |
| 0.616 | 193.55 | 12000 | 0.8892 | 0.5864 | 0.1527 |
| 0.5815 | 212.9 | 13200 | 0.8839 | 0.5772 | 0.1503 |
| 0.55 | 232.26 | 14400 | 0.8905 | 0.5665 | 0.1436 |
| 0.5173 | 251.61 | 15600 | 0.8995 | 0.5471 | 0.1417 |
| 0.4969 | 270.97 | 16800 | 0.8633 | 0.5325 | 0.1334 |
| 0.4803 | 290.32 | 18000 | 0.9074 | 0.5253 | 0.1352 |
| 0.4596 | 309.68 | 19200 | 0.9159 | 0.5146 | 0.1294 |
| 0.4415 | 329.03 | 20400 | 0.9055 | 0.5189 | 0.1314 |
| 0.434 | 348.39 | 21600 | 0.9435 | 0.5208 | 0.1314 |
| 0.4199 | 367.74 | 22800 | 0.9199 | 0.5136 | 0.1290 |
| 0.4008 | 387.1 | 24000 | 0.9342 | 0.5174 | 0.1303 |
| 0.4051 | 406.45 | 25200 | 0.9436 | 0.5132 | 0.1292 |
| 0.3861 | 425.81 | 26400 | 0.9417 | 0.5084 | 0.1283 |
| 0.3738 | 445.16 | 27600 | 0.9573 | 0.5079 | 0.1299 |
| 0.3768 | 464.52 | 28800 | 0.9682 | 0.5062 | 0.1289 |
| 0.3647 | 483.87 | 30000 | 0.9643 | 0.5037 | 0.1278 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"language": ["hsb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Upper Sorbian comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": 56.3, "name": "Test WER"}, {"type": "cer", "value": 14.3, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-hsb-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"hsb",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hsb"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Upper Sorbian wav2vec2-xls-r-300m-hsb-cv8
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9643
* Wer: 0.5037
* Cer: 0.1278
Evaluation
----------
The model can be evaluated using the attached 'URL' script:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* 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
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 500
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
114,
130,
4,
40
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 500\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
# wav2vec2-xls-r-300m-pl-cv8
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 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.1716
- Wer: 0.1697
- Cer: 0.0385
The `eval.py` script results are:
WER: 0.16970531733661967
CER: 0.03839135416519316
## Model description
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
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("mozilla-foundation/common_voice_8_0", "pl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training
## Training procedure
### Training hyperparameters
The following hyperparameters were used:
- learning_rate: 1e-4
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
- mixed_precision_training: Native AMP
The training was interrupted after 3250 steps.
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
{"language": ["pl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Polish comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "pl"}, "metrics": [{"type": "wer", "value": 17.0, "name": "Test WER"}, {"type": "cer", "value": 3.8, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "pl"}, "metrics": [{"type": "wer", "value": 38.97, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "pl"}, "metrics": [{"type": "wer", "value": 46.05, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-pl-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"pl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #pl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-pl-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.1716
- Wer: 0.1697
- Cer: 0.0385
The 'URL' script results are:
WER: 0.16970531733661967
CER: 0.03839135416519316
## Model description
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated using the attached 'URL' script:
## Training and evaluation data
The Common Voice 8.0 'train' and 'validation' datasets were used for training
## Training procedure
### Training hyperparameters
The following hyperparameters were used:
- learning_rate: 1e-4
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
- mixed_precision_training: Native AMP
The training was interrupted after 3250 steps.
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
[
"# wav2vec2-xls-r-300m-pl-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\nIt achieves the following results on the evaluation set while training:\n- Loss: 0.1716\n- Wer: 0.1697\n- Cer: 0.0385\n\nThe 'URL' script results are:\nWER: 0.16970531733661967\nCER: 0.03839135416519316",
"## Model description\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated using the attached 'URL' script:",
"## Training and evaluation data\n\nThe Common Voice 8.0 'train' and 'validation' datasets were used for training",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used:\n\n- learning_rate: 1e-4\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 1\n- total_train_batch_size: 640\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: 150\n- mixed_precision_training: Native AMP\n\nThe training was interrupted after 3250 steps.",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.17.1.dev0\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #pl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-pl-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\nIt achieves the following results on the evaluation set while training:\n- Loss: 0.1716\n- Wer: 0.1697\n- Cer: 0.0385\n\nThe 'URL' script results are:\nWER: 0.16970531733661967\nCER: 0.03839135416519316",
"## Model description\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated using the attached 'URL' script:",
"## Training and evaluation data\n\nThe Common Voice 8.0 'train' and 'validation' datasets were used for training",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used:\n\n- learning_rate: 1e-4\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 1\n- total_train_batch_size: 640\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: 150\n- mixed_precision_training: Native AMP\n\nThe training was interrupted after 3250 steps.",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.17.1.dev0\n- Tokenizers 0.11.0"
] |
[
105,
110,
68,
17,
27,
3,
149,
41
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #pl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# wav2vec2-xls-r-300m-pl-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\nIt achieves the following results on the evaluation set while training:\n- Loss: 0.1716\n- Wer: 0.1697\n- Cer: 0.0385\n\nThe 'URL' script results are:\nWER: 0.16970531733661967\nCER: 0.03839135416519316## Model description\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated using the attached 'URL' script:## Training and evaluation data\n\nThe Common Voice 8.0 'train' and 'validation' datasets were used for training## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used:\n\n- learning_rate: 1e-4\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 1\n- total_train_batch_size: 640\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: 150\n- mixed_precision_training: Native AMP\n\nThe training was interrupted after 3250 steps."
] |
[
-0.12744306027889252,
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] |
null | null |
transformers
|
# wav2vec2-xls-r-300m-cs-cv8
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 8.0 dataset.
It achieves the following results on the evaluation set:
- WER: 0.49575384615384616
- CER: 0.13333333333333333
## 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("mozilla-foundation/common_voice_8_0", "sk", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sk-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config sk
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-4
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
{"language": ["sk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Slovak comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sk"}, "metrics": [{"type": "wer", "value": 49.6, "name": "Test WER"}, {"type": "cer", "value": 13.3, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sk"}, "metrics": [{"type": "wer", "value": 81.7, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sk"}, "metrics": [{"type": "wer", "value": 80.26, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-sk-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"sk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sk"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #sk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-cs-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set:
- WER: 0.49575384615384616
- CER: 0.13333333333333333
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated using the attached 'URL' script:
## Training and evaluation data
The Common Voice 8.0 'train' and 'validation' datasets were used for training
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-4
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
[
"# wav2vec2-xls-r-300m-cs-cv8\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\r\nIt achieves the following results on the evaluation set:\r\n\r\n- WER: 0.49575384615384616\r\n- CER: 0.13333333333333333",
"## Usage\r\n\r\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\r\n\r\nThe model can be evaluated using the attached 'URL' script:",
"## Training and evaluation data\r\n\r\nThe Common Voice 8.0 'train' and 'validation' datasets were used for training",
"### Training hyperparameters\r\n\r\nThe following hyperparameters were used during training:\r\n\r\n- learning_rate: 7e-4\r\n- train_batch_size: 32\r\n- eval_batch_size: 8\r\n- seed: 42\r\n- gradient_accumulation_steps: 20\r\n- total_train_batch_size: 640\r\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\r\n- lr_scheduler_type: linear\r\n- lr_scheduler_warmup_steps: 500\r\n- num_epochs: 50\r\n- mixed_precision_training: Native AMP",
"### Framework versions\r\n\r\n- Transformers 4.16.0.dev0\r\n- Pytorch 1.10.1+cu102\r\n- Datasets 1.17.1.dev0\r\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #sk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-cs-cv8\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\r\nIt achieves the following results on the evaluation set:\r\n\r\n- WER: 0.49575384615384616\r\n- CER: 0.13333333333333333",
"## Usage\r\n\r\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\r\n\r\nThe model can be evaluated using the attached 'URL' script:",
"## Training and evaluation data\r\n\r\nThe Common Voice 8.0 'train' and 'validation' datasets were used for training",
"### Training hyperparameters\r\n\r\nThe following hyperparameters were used during training:\r\n\r\n- learning_rate: 7e-4\r\n- train_batch_size: 32\r\n- eval_batch_size: 8\r\n- seed: 42\r\n- gradient_accumulation_steps: 20\r\n- total_train_batch_size: 640\r\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\r\n- lr_scheduler_type: linear\r\n- lr_scheduler_warmup_steps: 500\r\n- num_epochs: 50\r\n- mixed_precision_training: Native AMP",
"### Framework versions\r\n\r\n- Transformers 4.16.0.dev0\r\n- Pytorch 1.10.1+cu102\r\n- Datasets 1.17.1.dev0\r\n- Tokenizers 0.11.0"
] |
[
105,
86,
20,
17,
27,
141,
41
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #sk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# wav2vec2-xls-r-300m-cs-cv8\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset.\r\nIt achieves the following results on the evaluation set:\r\n\r\n- WER: 0.49575384615384616\r\n- CER: 0.13333333333333333## Usage\r\n\r\nThe model can be used directly (without a language model) as follows:## Evaluation\r\n\r\nThe model can be evaluated using the attached 'URL' script:## Training and evaluation data\r\n\r\nThe Common Voice 8.0 'train' and 'validation' datasets were used for training### Training hyperparameters\r\n\r\nThe following hyperparameters were used during training:\r\n\r\n- learning_rate: 7e-4\r\n- train_batch_size: 32\r\n- eval_batch_size: 8\r\n- seed: 42\r\n- gradient_accumulation_steps: 20\r\n- total_train_batch_size: 640\r\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\r\n- lr_scheduler_type: linear\r\n- lr_scheduler_warmup_steps: 500\r\n- num_epochs: 50\r\n- mixed_precision_training: Native AMP### Framework versions\r\n\r\n- Transformers 4.16.0.dev0\r\n- Pytorch 1.10.1+cu102\r\n- Datasets 1.17.1.dev0\r\n- Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
# Serbian wav2vec2-xls-r-300m-sr-cv8
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.
It achieves the following results on the evaluation set:
- Loss: 1.7302
- Wer: 0.4825
- Cer: 0.1847
Evaluation on mozilla-foundation/common_voice_8_0 gave the following results:
- WER: 0.48530097993467103
- CER: 0.18413288165227845
Evaluation on speech-recognition-community-v2/dev_data gave the following results:
- WER: 0.9718373107518604
- CER: 0.8302740620263108
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sr-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config sr
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 300
- num_epochs: 800
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 5.6536 | 15.0 | 1200 | 2.9744 | 1.0 | 1.0 |
| 2.7935 | 30.0 | 2400 | 1.6613 | 0.8998 | 0.4670 |
| 1.6538 | 45.0 | 3600 | 0.9248 | 0.6918 | 0.2699 |
| 1.2446 | 60.0 | 4800 | 0.9151 | 0.6452 | 0.2398 |
| 1.0766 | 75.0 | 6000 | 0.9110 | 0.5995 | 0.2207 |
| 0.9548 | 90.0 | 7200 | 1.0273 | 0.5921 | 0.2149 |
| 0.8919 | 105.0 | 8400 | 0.9929 | 0.5646 | 0.2117 |
| 0.8185 | 120.0 | 9600 | 1.0850 | 0.5483 | 0.2069 |
| 0.7692 | 135.0 | 10800 | 1.1001 | 0.5394 | 0.2055 |
| 0.7249 | 150.0 | 12000 | 1.1018 | 0.5380 | 0.1958 |
| 0.6786 | 165.0 | 13200 | 1.1344 | 0.5114 | 0.1941 |
| 0.6432 | 180.0 | 14400 | 1.1516 | 0.5054 | 0.1905 |
| 0.6009 | 195.0 | 15600 | 1.3149 | 0.5324 | 0.1991 |
| 0.5773 | 210.0 | 16800 | 1.2468 | 0.5124 | 0.1903 |
| 0.559 | 225.0 | 18000 | 1.2186 | 0.4956 | 0.1922 |
| 0.5298 | 240.0 | 19200 | 1.4483 | 0.5333 | 0.2085 |
| 0.5136 | 255.0 | 20400 | 1.2871 | 0.4802 | 0.1846 |
| 0.4824 | 270.0 | 21600 | 1.2891 | 0.4974 | 0.1885 |
| 0.4669 | 285.0 | 22800 | 1.3283 | 0.4942 | 0.1878 |
| 0.4511 | 300.0 | 24000 | 1.4502 | 0.5002 | 0.1994 |
| 0.4337 | 315.0 | 25200 | 1.4714 | 0.5035 | 0.1911 |
| 0.4221 | 330.0 | 26400 | 1.4971 | 0.5124 | 0.1962 |
| 0.3994 | 345.0 | 27600 | 1.4473 | 0.5007 | 0.1920 |
| 0.3892 | 360.0 | 28800 | 1.3904 | 0.4937 | 0.1887 |
| 0.373 | 375.0 | 30000 | 1.4971 | 0.4946 | 0.1902 |
| 0.3657 | 390.0 | 31200 | 1.4208 | 0.4900 | 0.1821 |
| 0.3559 | 405.0 | 32400 | 1.4648 | 0.4895 | 0.1835 |
| 0.3476 | 420.0 | 33600 | 1.4848 | 0.4946 | 0.1829 |
| 0.3276 | 435.0 | 34800 | 1.5597 | 0.4979 | 0.1873 |
| 0.3193 | 450.0 | 36000 | 1.7329 | 0.5040 | 0.1980 |
| 0.3078 | 465.0 | 37200 | 1.6379 | 0.4937 | 0.1882 |
| 0.3058 | 480.0 | 38400 | 1.5878 | 0.4942 | 0.1921 |
| 0.2987 | 495.0 | 39600 | 1.5590 | 0.4811 | 0.1846 |
| 0.2931 | 510.0 | 40800 | 1.6001 | 0.4825 | 0.1849 |
| 0.276 | 525.0 | 42000 | 1.7388 | 0.4942 | 0.1918 |
| 0.2702 | 540.0 | 43200 | 1.7037 | 0.4839 | 0.1866 |
| 0.2619 | 555.0 | 44400 | 1.6704 | 0.4755 | 0.1840 |
| 0.262 | 570.0 | 45600 | 1.6042 | 0.4751 | 0.1865 |
| 0.2528 | 585.0 | 46800 | 1.6402 | 0.4821 | 0.1865 |
| 0.2442 | 600.0 | 48000 | 1.6693 | 0.4886 | 0.1862 |
| 0.244 | 615.0 | 49200 | 1.6203 | 0.4765 | 0.1792 |
| 0.2388 | 630.0 | 50400 | 1.6829 | 0.4830 | 0.1828 |
| 0.2362 | 645.0 | 51600 | 1.8100 | 0.4928 | 0.1888 |
| 0.2224 | 660.0 | 52800 | 1.7746 | 0.4932 | 0.1899 |
| 0.2218 | 675.0 | 54000 | 1.7752 | 0.4946 | 0.1901 |
| 0.2201 | 690.0 | 55200 | 1.6775 | 0.4788 | 0.1844 |
| 0.2147 | 705.0 | 56400 | 1.7085 | 0.4844 | 0.1851 |
| 0.2103 | 720.0 | 57600 | 1.7624 | 0.4848 | 0.1864 |
| 0.2101 | 735.0 | 58800 | 1.7213 | 0.4783 | 0.1835 |
| 0.1983 | 750.0 | 60000 | 1.7452 | 0.4848 | 0.1856 |
| 0.2015 | 765.0 | 61200 | 1.7525 | 0.4872 | 0.1869 |
| 0.1969 | 780.0 | 62400 | 1.7443 | 0.4844 | 0.1852 |
| 0.2043 | 795.0 | 63600 | 1.7302 | 0.4825 | 0.1847 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"language": ["sr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0", {"name": "Serbian comodoro Wav2Vec2 XLSR 300M CV8", "results": [{"task": {"name": "Automatic Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sr"}, "metrics": [{"name": "Test WER", "type": "wer", "value": 48.5}, {"name": "Test CER", "type": "cer", "value": 18.4}]}]}], "model-index": [{"name": "wav2vec2-xls-r-300m-sr-cv8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8.0", "type": "mozilla-foundation/common_voice_8_0", "args": "sr"}, "metrics": [{"type": "wer", "value": 48.53, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sr"}, "metrics": [{"type": "wer", "value": 97.43, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sr"}, "metrics": [{"type": "wer", "value": 96.69, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-sr-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"sr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #sr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Serbian wav2vec2-xls-r-300m-sr-cv8
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7302
* Wer: 0.4825
* Cer: 0.1847
Evaluation on mozilla-foundation/common\_voice\_8\_0 gave the following results:
* WER: 0.48530097993467103
* CER: 0.18413288165227845
Evaluation on speech-recognition-community-v2/dev\_data gave the following results:
* WER: 0.9718373107518604
* CER: 0.8302740620263108
The model can be evaluated using the attached 'URL' script:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* 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
* lr\_scheduler\_warmup\_steps: 300
* num\_epochs: 800
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.1+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps: 300\n* num\\_epochs: 800\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #sr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps: 300\n* num\\_epochs: 800\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
104,
130,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #xlsr-fine-tuning-week #hf-asr-leaderboard #sr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps: 300\n* num\\_epochs: 800\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
# wav2vec2-xls-r-300m-west-slavic-cv8
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 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.
Evaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.
### Evaluation script
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-west-slavic-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config {lang}
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"language": ["cs", "hsb", "pl", "sk", "sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-west-slavic-cv8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "cs"}, "metrics": [{"type": "wer", "value": 53.5, "name": "Test WER"}, {"type": "cer", "value": 14.7, "name": "Test CER"}, {"type": "wer", "value": 81.7, "name": "Test WER"}, {"type": "cer", "value": 21.2, "name": "Test CER"}, {"type": "wer", "value": 60.2, "name": "Test WER"}, {"type": "cer", "value": 15.6, "name": "Test CER"}, {"type": "wer", "value": 69.6, "name": "Test WER"}, {"type": "cer", "value": 20.7, "name": "Test CER"}, {"type": "wer", "value": 73.2, "name": "Test WER"}, {"type": "cer", "value": 23.2, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 84.11, "name": "Test WER"}, {"type": "wer", "value": 65.3, "name": "Test WER"}, {"type": "wer", "value": 88.37, "name": "Test WER"}, {"type": "wer", "value": 87.69, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "cs"}, "metrics": [{"type": "wer", "value": 75.99, "name": "Test WER"}, {"type": "wer", "value": 72.0, "name": "Test WER"}, {"type": "wer", "value": 89.08, "name": "Test WER"}, {"type": "wer", "value": 87.89, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
comodoro/wav2vec2-xls-r-300m-west-slavic-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"hsb",
"pl",
"sk",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"cs",
"hsb",
"pl",
"sk",
"sl"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #hsb #pl #sk #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-west-slavic-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.
Evaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.
### Evaluation script
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# wav2vec2-xls-r-300m-west-slavic-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.\n\nEvaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.",
"### Evaluation script",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 32\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- lr_scheduler_warmup_steps: 500\n- num_epochs: 50\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #hsb #pl #sk #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-west-slavic-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.\n\nEvaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.",
"### Evaluation script",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 32\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- lr_scheduler_warmup_steps: 500\n- num_epochs: 50\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
132,
137,
5,
117,
40
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #xlsr-fine-tuning-week #cs #hsb #pl #sk #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# wav2vec2-xls-r-300m-west-slavic-cv8\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Common Voice 8 dataset of five similar languages with similar scripts: Czech, Slovak, Polish, Slovenian and Upper Sorbian. Training and validation sets were concatenated and shuffled.\n\nEvaluation set used for training was concatenated from the respective test sets and shuffled while limiting each language to at most 2000 samples. During training, cca WER 70 was achieved on this set.### Evaluation script### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 32\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- lr_scheduler_warmup_steps: 500\n- num_epochs: 50\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\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. -->
# distilroberta-base-finetuned-toxic
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2768
## 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: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5338 | 1.0 | 313 | 2.3127 |
| 2.4482 | 2.0 | 626 | 2.2985 |
| 2.4312 | 3.0 | 939 | 2.2411 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-toxic", "results": []}]}
|
fill-mask
|
conjuring92/distilroberta-base-finetuned-toxic
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"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 #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilroberta-base-finetuned-toxic
==================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2768
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: 32
* eval\_batch\_size: 32
* seed: 42
* 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 4.16.0
* Pytorch 1.10.0
* Datasets 1.18.1
* 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: 32\n* eval\\_batch\\_size: 32\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\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0\n* Pytorch 1.10.0\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 32\n* eval\\_batch\\_size: 32\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\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0\n* Pytorch 1.10.0\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
56,
113,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #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: 32\n* eval\\_batch\\_size: 32\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\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0\n* Pytorch 1.10.0\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Snape DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
conniezyj/DialoGPT-small-snape
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Snape DialoGPT Model
|
[
"# Snape DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Snape DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Snape DialoGPT Model"
] |
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] |
null | null |
transformers
|
Named-entity recognition model trained on the I2B2 training data set for PHI.
|
{}
|
token-classification
|
connorboyle/bert-ner-i2b2
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
Named-entity recognition model trained on the I2B2 training data set for PHI.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
42
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
hello
|
{}
|
text-classification
|
conversify/response-score
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
hello
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
39
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
# LIMIT-BERT
Code and model for the *EMNLP 2020 Findings* paper:
[LIMIT-BERT: Linguistic Informed Multi-task BERT](https://arxiv.org/abs/1910.14296))
## Contents
1. [Requirements](#Requirements)
2. [Training](#Training)
## Requirements
* Python 3.6 or higher.
* Cython 0.25.2 or any compatible version.
* [PyTorch](http://pytorch.org/) 1.0.0+.
* [EVALB](http://nlp.cs.nyu.edu/evalb/). Before starting, run `make` inside the `EVALB/` directory to compile an `evalb` executable. This will be called from Python for evaluation.
* [pytorch-transformers](https://github.com/huggingface/pytorch-transformers) PyTorch 1.0.0+ or any compatible version.
#### Pre-trained Models (PyTorch)
The following pre-trained models are available for download from Google Drive:
* [`LIMIT-BERT`](https://drive.google.com/open?id=1fm0cK2A91iLG3lCpwowCCQSALnWS2X4i):
PyTorch version, same setting with BERT-Large-WWM，loading model with [pytorch-transformers](https://github.com/huggingface/pytorch-transformers).
## How to use
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cooelf/limitbert")
model = AutoModel.from_pretrained("cooelf/limitbert")
```
Please see our original repo for the training scripts.
https://github.com/cooelf/LIMIT-BERT
## Training
To train LIMIT-BERT, simply run:
```
sh run_limitbert.sh
```
### Evaluation Instructions
To test after setting model path:
```
sh test_bert.sh
```
## Citation
```
@article{zhou2019limit,
title={{LIMIT-BERT}: Linguistic informed multi-task {BERT}},
author={Zhou, Junru and Zhang, Zhuosheng and Zhao, Hai},
journal={arXiv preprint arXiv:1910.14296},
year={2019}
}
```
|
{}
| null |
cooelf/limitbert
|
[
"transformers",
"pytorch",
"arxiv:1910.14296",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1910.14296"
] |
[] |
TAGS
#transformers #pytorch #arxiv-1910.14296 #endpoints_compatible #region-us
|
# LIMIT-BERT
Code and model for the *EMNLP 2020 Findings* paper:
LIMIT-BERT: Linguistic Informed Multi-task BERT)
## Contents
1. Requirements
2. Training
## Requirements
* Python 3.6 or higher.
* Cython 0.25.2 or any compatible version.
* PyTorch 1.0.0+.
* EVALB. Before starting, run 'make' inside the 'EVALB/' directory to compile an 'evalb' executable. This will be called from Python for evaluation.
* pytorch-transformers PyTorch 1.0.0+ or any compatible version.
#### Pre-trained Models (PyTorch)
The following pre-trained models are available for download from Google Drive:
* 'LIMIT-BERT':
PyTorch version, same setting with BERT-Large-WWM，loading model with pytorch-transformers.
## How to use
Please see our original repo for the training scripts.
URL
## Training
To train LIMIT-BERT, simply run:
### Evaluation Instructions
To test after setting model path:
|
[
"# LIMIT-BERT\n\nCode and model for the *EMNLP 2020 Findings* paper: \n\nLIMIT-BERT: Linguistic Informed Multi-task BERT)",
"## Contents\n\n1. Requirements\n2. Training",
"## Requirements\n\n* Python 3.6 or higher.\n* Cython 0.25.2 or any compatible version.\n* PyTorch 1.0.0+. \n* EVALB. Before starting, run 'make' inside the 'EVALB/' directory to compile an 'evalb' executable. This will be called from Python for evaluation. \n* pytorch-transformers PyTorch 1.0.0+ or any compatible version.",
"#### Pre-trained Models (PyTorch)\nThe following pre-trained models are available for download from Google Drive:\n* 'LIMIT-BERT': \n PyTorch version, same setting with BERT-Large-WWM，loading model with pytorch-transformers.",
"## How to use\n\n\n\nPlease see our original repo for the training scripts.\n\nURL",
"## Training\n\nTo train LIMIT-BERT, simply run:",
"### Evaluation Instructions\n\nTo test after setting model path:"
] |
[
"TAGS\n#transformers #pytorch #arxiv-1910.14296 #endpoints_compatible #region-us \n",
"# LIMIT-BERT\n\nCode and model for the *EMNLP 2020 Findings* paper: \n\nLIMIT-BERT: Linguistic Informed Multi-task BERT)",
"## Contents\n\n1. Requirements\n2. Training",
"## Requirements\n\n* Python 3.6 or higher.\n* Cython 0.25.2 or any compatible version.\n* PyTorch 1.0.0+. \n* EVALB. Before starting, run 'make' inside the 'EVALB/' directory to compile an 'evalb' executable. This will be called from Python for evaluation. \n* pytorch-transformers PyTorch 1.0.0+ or any compatible version.",
"#### Pre-trained Models (PyTorch)\nThe following pre-trained models are available for download from Google Drive:\n* 'LIMIT-BERT': \n PyTorch version, same setting with BERT-Large-WWM，loading model with pytorch-transformers.",
"## How to use\n\n\n\nPlease see our original repo for the training scripts.\n\nURL",
"## Training\n\nTo train LIMIT-BERT, simply run:",
"### Evaluation Instructions\n\nTo test after setting model path:"
] |
[
30,
39,
10,
89,
66,
16,
13,
14
] |
[
"passage: TAGS\n#transformers #pytorch #arxiv-1910.14296 #endpoints_compatible #region-us \n# LIMIT-BERT\n\nCode and model for the *EMNLP 2020 Findings* paper: \n\nLIMIT-BERT: Linguistic Informed Multi-task BERT)## Contents\n\n1. Requirements\n2. Training## Requirements\n\n* Python 3.6 or higher.\n* Cython 0.25.2 or any compatible version.\n* PyTorch 1.0.0+. \n* EVALB. Before starting, run 'make' inside the 'EVALB/' directory to compile an 'evalb' executable. This will be called from Python for evaluation. \n* pytorch-transformers PyTorch 1.0.0+ or any compatible version.#### Pre-trained Models (PyTorch)\nThe following pre-trained models are available for download from Google Drive:\n* 'LIMIT-BERT': \n PyTorch version, same setting with BERT-Large-WWM，loading model with pytorch-transformers.## How to use\n\n\n\nPlease see our original repo for the training scripts.\n\nURL## Training\n\nTo train LIMIT-BERT, simply run:### Evaluation Instructions\n\nTo test after setting model path:"
] |
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null | null |
transformers
|
# Cicero-Similis
## Model description
A Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
## Intended uses & limitations
#### How to use
Normalize text using JV Replacement and tokenize using CLTK to separate enclitics such as "-que", then:
```
from transformers import BertForMaskedLM, AutoTokenizer, FillMaskPipeline
tokenizer = AutoTokenizer.from_pretrained("cook/cicero-similis")
model = BertForMaskedLM.from_pretrained("cook/cicero-similis")
fill_mask = FillMaskPipeline(model=model, tokenizer=tokenizer, top_k=10_000)
# Cicero, De Re Publica, VI, 32, 2
# "animal" is found in A, Q, PhD manuscripts
# 'anima' H^1 Macr. et codd. Tusc.
results = fill_mask("inanimum est enim omne quod pulsu agitatur externo; quod autem est [MASK],")
```
#### Limitations and bias
Currently the model training data excludes modern and 19th century texts, but that weakness is the model's strength; it's not aimed to be a one-size-fits-all model.
## Training data
Trained on the corpora Phi5, Tesserae, Thomas Aquinas, and Patrologes Latina.
## Training procedure
5 epochs, masked language modeling .15, effective batch size 32
## Eval results
A novel evaluation metric is proposed in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
### BibTeX entry and citation info
TODO
_What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
|
{"language": ["la"], "license": "apache-2.0", "tags": ["language model"], "datasets": ["Tesserae", "Phi5", "Thomas Aquinas", "Patrologia Latina"]}
|
fill-mask
|
cook/cicero-similis
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"language model",
"la",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"la"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #language model #la #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cicero-Similis
## Model description
A Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
## Intended uses & limitations
#### How to use
Normalize text using JV Replacement and tokenize using CLTK to separate enclitics such as "-que", then:
#### Limitations and bias
Currently the model training data excludes modern and 19th century texts, but that weakness is the model's strength; it's not aimed to be a one-size-fits-all model.
## Training data
Trained on the corpora Phi5, Tesserae, Thomas Aquinas, and Patrologes Latina.
## Training procedure
5 epochs, masked language modeling .15, effective batch size 32
## Eval results
A novel evaluation metric is proposed in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
### BibTeX entry and citation info
TODO
_What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,
Published in Ciceroniana On Line, Vol. V, #2.
|
[
"# Cicero-Similis",
"## Model description\n\nA Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.",
"## Intended uses & limitations",
"#### How to use\n\nNormalize text using JV Replacement and tokenize using CLTK to separate enclitics such as \"-que\", then:",
"#### Limitations and bias\n\nCurrently the model training data excludes modern and 19th century texts, but that weakness is the model's strength; it's not aimed to be a one-size-fits-all model.",
"## Training data\n\nTrained on the corpora Phi5, Tesserae, Thomas Aquinas, and Patrologes Latina.",
"## Training procedure\n\n5 epochs, masked language modeling .15, effective batch size 32",
"## Eval results\nA novel evaluation metric is proposed in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.",
"### BibTeX entry and citation info\nTODO\n_What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #language model #la #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cicero-Similis",
"## Model description\n\nA Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.",
"## Intended uses & limitations",
"#### How to use\n\nNormalize text using JV Replacement and tokenize using CLTK to separate enclitics such as \"-que\", then:",
"#### Limitations and bias\n\nCurrently the model training data excludes modern and 19th century texts, but that weakness is the model's strength; it's not aimed to be a one-size-fits-all model.",
"## Training data\n\nTrained on the corpora Phi5, Tesserae, Thomas Aquinas, and Patrologes Latina.",
"## Training procedure\n\n5 epochs, masked language modeling .15, effective batch size 32",
"## Eval results\nA novel evaluation metric is proposed in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.",
"### BibTeX entry and citation info\nTODO\n_What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2."
] |
[
55,
5,
69,
9,
35,
54,
26,
23,
55,
53
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #language model #la #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cicero-Similis## Model description\n\nA Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.## Intended uses & limitations#### How to use\n\nNormalize text using JV Replacement and tokenize using CLTK to separate enclitics such as \"-que\", then:#### Limitations and bias\n\nCurrently the model training data excludes modern and 19th century texts, but that weakness is the model's strength; it's not aimed to be a one-size-fits-all model.## Training data\n\nTrained on the corpora Phi5, Tesserae, Thomas Aquinas, and Patrologes Latina.## Training procedure\n\n5 epochs, masked language modeling .15, effective batch size 32## Eval results\nA novel evaluation metric is proposed in the paper _What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2.### BibTeX entry and citation info\nTODO\n_What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model_ by Todd Cook,\nPublished in Ciceroniana On Line, Vol. V, #2."
] |
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] |
null | null |
transformers
|
# Joreyar DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
cookirei/DialoGPT-medium-Joreyar
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Joreyar DialoGPT Model
|
[
"# Joreyar DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Joreyar DialoGPT Model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Joreyar DialoGPT Model"
] |
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] |
null | null |
transformers
|
This is the SciBERT pretrained language model further fine-tuned on masked language modeling and cite-worthiness detection on the [CiteWorth](https://github.com/copenlu/cite-worth) dataset. Note that this model should be used for further fine-tuning on downstream scientific document understanding tasks.
|
{}
|
feature-extraction
|
copenlu/citebert
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
|
This is the SciBERT pretrained language model further fine-tuned on masked language modeling and cite-worthiness detection on the CiteWorth dataset. Note that this model should be used for further fine-tuning on downstream scientific document understanding tasks.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
29
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Uzbek news category classifier (based on UzBERT)
UzBERT fine-tuned to classify news articles into one of the following
categories:
- дунё
- жамият
- жиноят
- иқтисодиёт
- маданият
- реклама
- саломатлик
- сиёсат
- спорт
- фан ва техника
- шоу-бизнес
## How to use
```python
>>> from transformers import pipeline
>>> classifier = pipeline('text-classification', model='coppercitylabs/uzbek-news-category-classifier')
>>> text = """Маҳоратли пара-енгил атлетикачимиз Ҳусниддин Норбеков Токио-2020 Паралимпия ўйинларида ғалаба қозониб, делегациямиз ҳисобига навбатдаги олтин медални келтирди. Бу ҳақда МОҚ хабар берди.
Норбеков ҳозиргина ядро улоқтириш дастурида ўз ғалабасини тантана қилди. Ушбу машқда вакилимиз 16:13 метр натижа билан энг яхши кўрсаткични қайд этди.
Шу тариқа, делегациямиз ҳисобидаги медаллар сони 16 (6 та олтин, 4 та кумуш ва 6 та бронза) тага етди. Кейинги кун дастурларида иштирок этадиган ҳамюртларимизга омад тилаб қоламиз!"""
>>> classifier(text)
[{'label': 'спорт', 'score': 0.9865401983261108}]
```
## Fine-tuning data
Fine-tuned on ~60K news articles for 3 epochs.
|
{"language": "uz", "license": "mit", "tags": ["uzbek", "cyrillic", "news category classifier"], "datasets": ["webcrawl"]}
|
text-classification
|
coppercitylabs/uzbek-news-category-classifier
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"uzbek",
"cyrillic",
"news category classifier",
"uz",
"dataset:webcrawl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"uz"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #uzbek #cyrillic #news category classifier #uz #dataset-webcrawl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Uzbek news category classifier (based on UzBERT)
UzBERT fine-tuned to classify news articles into one of the following
categories:
- дунё
- жамият
- жиноят
- иқтисодиёт
- маданият
- реклама
- саломатлик
- сиёсат
- спорт
- фан ва техника
- шоу-бизнес
## How to use
## Fine-tuning data
Fine-tuned on ~60K news articles for 3 epochs.
|
[
"# Uzbek news category classifier (based on UzBERT)\n\nUzBERT fine-tuned to classify news articles into one of the following\ncategories:\n\n- дунё\n- жамият\n- жиноят\n- иқтисодиёт\n- маданият\n- реклама\n- саломатлик\n- сиёсат\n- спорт\n- фан ва техника\n- шоу-бизнес",
"## How to use",
"## Fine-tuning data\nFine-tuned on ~60K news articles for 3 epochs."
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #uzbek #cyrillic #news category classifier #uz #dataset-webcrawl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Uzbek news category classifier (based on UzBERT)\n\nUzBERT fine-tuned to classify news articles into one of the following\ncategories:\n\n- дунё\n- жамият\n- жиноят\n- иқтисодиёт\n- маданият\n- реклама\n- саломатлик\n- сиёсат\n- спорт\n- фан ва техника\n- шоу-бизнес",
"## How to use",
"## Fine-tuning data\nFine-tuned on ~60K news articles for 3 epochs."
] |
[
71,
77,
4,
23
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #uzbek #cyrillic #news category classifier #uz #dataset-webcrawl #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Uzbek news category classifier (based on UzBERT)\n\nUzBERT fine-tuned to classify news articles into one of the following\ncategories:\n\n- дунё\n- жамият\n- жиноят\n- иқтисодиёт\n- маданият\n- реклама\n- саломатлик\n- сиёсат\n- спорт\n- фан ва техника\n- шоу-бизнес## How to use## Fine-tuning data\nFine-tuned on ~60K news articles for 3 epochs."
] |
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] |
null | null |
transformers
|
# UzBERT base model (uncased)
Pretrained model on Uzbek language (Cyrillic script) using a masked
language modeling and next sentence prediction objectives.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='coppercitylabs/uzbert-base-uncased')
>>> unmasker("Алишер Навоий – улуғ ўзбек ва бошқа туркий халқларнинг [MASK], мутафаккири ва давлат арбоби бўлган.")
[
{
'token_str': 'шоири',
'token': 13587,
'score': 0.7974384427070618,
'sequence': 'алишер навоий – улуғ ўзбек ва бошқа туркий халқларнинг шоири, мутафаккир ##и ва давлат арбоби бўлган.'
},
{
'token_str': 'олими',
'token': 18500,
'score': 0.09166576713323593,
'sequence': 'алишер навоий – улуғ ўзбек ва бошқа туркий халқларнинг олими, мутафаккир ##и ва давлат арбоби бўлган.'
},
{
'token_str': 'асосчиси',
'token': 7469,
'score': 0.02451123297214508,
'sequence': 'алишер навоий – улуғ ўзбек ва бошқа туркий халқларнинг асосчиси, мутафаккир ##и ва давлат арбоби бўлган.'
},
{
'token_str': 'ёзувчиси',
'token': 22439,
'score': 0.017601722851395607,
'sequence': 'алишер навоий – улуғ ўзбек ва бошқа туркий халқларнинг ёзувчиси, мутафаккир ##и ва давлат арбоби бўлган.'
},
{
'token_str': 'устози',
'token': 11494,
'score': 0.010115668177604675,
'sequence': 'алишер навоий – улуғ ўзбек ва бошқа туркий халқларнинг устози, мутафаккир ##и ва давлат арбоби бўлган.'
}
]
```
## Training data
UzBERT model was pretrained on \~625K news articles (\~142M words).
## BibTeX entry and citation info
```bibtex
@misc{mansurov2021uzbert,
title={{UzBERT: pretraining a BERT model for Uzbek}},
author={B. Mansurov and A. Mansurov},
year={2021},
eprint={2108.09814},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "uz", "license": "mit", "tags": ["uzbert", "uzbek", "bert", "cyrillic"], "datasets": ["webcrawl"]}
|
fill-mask
|
coppercitylabs/uzbert-base-uncased
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"uzbert",
"uzbek",
"cyrillic",
"uz",
"dataset:webcrawl",
"arxiv:2108.09814",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2108.09814"
] |
[
"uz"
] |
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #uzbert #uzbek #cyrillic #uz #dataset-webcrawl #arxiv-2108.09814 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# UzBERT base model (uncased)
Pretrained model on Uzbek language (Cyrillic script) using a masked
language modeling and next sentence prediction objectives.
## How to use
You can use this model directly with a pipeline for masked language modeling:
## Training data
UzBERT model was pretrained on \~625K news articles (\~142M words).
## BibTeX entry and citation info
|
[
"# UzBERT base model (uncased)\n\nPretrained model on Uzbek language (Cyrillic script) using a masked\nlanguage modeling and next sentence prediction objectives.",
"## How to use\n\nYou can use this model directly with a pipeline for masked language modeling:",
"## Training data\n\nUzBERT model was pretrained on \\~625K news articles (\\~142M words).",
"## BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #uzbert #uzbek #cyrillic #uz #dataset-webcrawl #arxiv-2108.09814 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# UzBERT base model (uncased)\n\nPretrained model on Uzbek language (Cyrillic script) using a masked\nlanguage modeling and next sentence prediction objectives.",
"## How to use\n\nYou can use this model directly with a pipeline for masked language modeling:",
"## Training data\n\nUzBERT model was pretrained on \\~625K news articles (\\~142M words).",
"## BibTeX entry and citation info"
] |
[
74,
41,
21,
27,
10
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #uzbert #uzbek #cyrillic #uz #dataset-webcrawl #arxiv-2108.09814 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# UzBERT base model (uncased)\n\nPretrained model on Uzbek language (Cyrillic script) using a masked\nlanguage modeling and next sentence prediction objectives.## How to use\n\nYou can use this model directly with a pipeline for masked language modeling:## Training data\n\nUzBERT model was pretrained on \\~625K news articles (\\~142M words).## BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# Rick Sanchez
|
{"tags": ["conversational"]}
|
text-generation
|
cosmic/DialoGPT-Rick
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez
|
[
"# Rick Sanchez"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez"
] |
[
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
cosmicray001/prod-harry
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model
|
[
"# Harry Potter DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
cosmicray001/small-harry
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model
|
[
"# Harry Potter DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
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] |
null | null |
transformers
|
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train model is [here](https://github.com/cosmoquester/transformers-bart-pretrain).
When you use the reference API, you must wrap the sentence with `[BOS]` and `[EOS]` like below example.
```
[BOS] 안녕하세요? 반가워요~~ [EOS]
```
You can also test mask filling performance using `[MASK]` token like this.
```
[BOS] [MASK] 먹었어? [EOS]
```
## Benchmark
<style>
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
</style>
<table>
<tr>
<th>Dataset</th>
<td>KLUE NLI dev</th>
<td>NSMC test</td>
<td>QuestionPair test</td>
<td colspan="2">KLUE TC dev</td>
<td colspan="3">KLUE STS dev</td>
<td colspan="3">KorSTS dev</td>
<td colspan="2">HateSpeech dev</td>
</tr>
<tr>
<th>Metric</th>
<!-- KLUE NLI -->
<td>Acc</th>
<!-- NSMC -->
<td>Acc</td>
<!-- QuestionPair -->
<td>Acc</td>
<!-- KLUE TC -->
<td>Acc</td>
<td>F1</td>
<!-- KLUE STS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- KorSTS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- HateSpeech -->
<td>Bias Acc</td>
<td>Hate Acc</td>
</tr>
<tr>
<th>Score</th>
<!-- KLUE NLI -->
<td>0.7390</th>
<!-- NSMC -->
<td>0.8877</td>
<!-- QuestionPair -->
<td>0.9208</td>
<!-- KLUE TC -->
<td>0.8667</td>
<td>0.8637</td>
<!-- KLUE STS -->
<td>0.7654</td>
<td>0.8090</td>
<td>0.8040</td>
<!-- KorSTS -->
<td>0.8067</td>
<td>0.7909</td>
<td>0.7784</td>
<!-- HateSpeech -->
<td>0.8280</td>
<td>0.5669</td>
</tr>
</table>
- The performance was measured using [the notebooks here](https://github.com/cosmoquester/transformers-bart-finetune) with colab.
## Used Datasets
### [모두의 말뭉치](https://corpus.korean.go.kr/)
- 일상 대화 말뭉치 2020
- 구어 말뭉치
- 문어 말뭉치
- 신문 말뭉치
### AIhub
- [개방데이터 전문분야말뭉치](https://aihub.or.kr/aidata/30717)
- [개방데이터 한국어대화요약](https://aihub.or.kr/aidata/30714)
- [개방데이터 감성 대화 말뭉치](https://aihub.or.kr/aidata/7978)
- [개방데이터 한국어 음성](https://aihub.or.kr/aidata/105)
- [개방데이터 한국어 SNS](https://aihub.or.kr/aidata/30718)
### [세종 말뭉치](https://ithub.korean.go.kr/)
|
{"language": "ko"}
|
text2text-generation
|
cosmoquester/bart-ko-base
|
[
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
|
Pretrained BART in Korean
=========================
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by TPU Research Cloud program.
The script which is used to pre-train model is here.
When you use the reference API, you must wrap the sentence with '[BOS]' and '[EOS]' like below example.
You can also test mask filling performance using '[MASK]' token like this.
Benchmark
---------
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
* The performance was measured using the notebooks here with colab.
Used Datasets
-------------
### 모두의 말뭉치
* 일상 대화 말뭉치 2020
* 구어 말뭉치
* 문어 말뭉치
* 신문 말뭉치
### AIhub
* 개방데이터 전문분야말뭉치
* 개방데이터 한국어대화요약
* 개방데이터 감성 대화 말뭉치
* 개방데이터 한국어 음성
* 개방데이터 한국어 SNS
### 세종 말뭉치
|
[
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
[
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
[
43,
32,
49,
6
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS### 세종 말뭉치"
] |
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] |
null | null |
transformers
|
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train model is [here](https://github.com/cosmoquester/transformers-bart-pretrain).
When you use the reference API, you must wrap the sentence with `[BOS]` and `[EOS]` like below example.
```
[BOS] 안녕하세요? 반가워요~~ [EOS]
```
You can also test mask filling performance using `[MASK]` token like this.
```
[BOS] [MASK] 먹었어? [EOS]
```
## Benchmark
<style>
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
</style>
<table>
<tr>
<th>Dataset</th>
<td>KLUE NLI dev</th>
<td>NSMC test</td>
<td>QuestionPair test</td>
<td colspan="2">KLUE TC dev</td>
<td colspan="3">KLUE STS dev</td>
<td colspan="3">KorSTS dev</td>
<td colspan="2">HateSpeech dev</td>
</tr>
<tr>
<th>Metric</th>
<!-- KLUE NLI -->
<td>Acc</th>
<!-- NSMC -->
<td>Acc</td>
<!-- QuestionPair -->
<td>Acc</td>
<!-- KLUE TC -->
<td>Acc</td>
<td>F1</td>
<!-- KLUE STS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- KorSTS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- HateSpeech -->
<td>Bias Acc</td>
<td>Hate Acc</td>
</tr>
<tr>
<th>Score</th>
<!-- KLUE NLI -->
<td>0.5253</th>
<!-- NSMC -->
<td>0.8425</td>
<!-- QuestionPair -->
<td>0.8945</td>
<!-- KLUE TC -->
<td>0.8047</td>
<td>0.7988</td>
<!-- KLUE STS -->
<td>0.7411</td>
<td>0.7471</td>
<td>0.7399</td>
<!-- KorSTS -->
<td>0.7725</td>
<td>0.6503</td>
<td>0.6191</td>
<!-- HateSpeech -->
<td>0.7537</td>
<td>0.5605</td>
</tr>
</table>
- The performance was measured using [the notebooks here](https://github.com/cosmoquester/transformers-bart-finetune) with colab.
## Used Datasets
### [모두의 말뭉치](https://corpus.korean.go.kr/)
- 일상 대화 말뭉치 2020
- 구어 말뭉치
- 문어 말뭉치
- 신문 말뭉치
### AIhub
- [개방데이터 전문분야말뭉치](https://aihub.or.kr/aidata/30717)
- [개방데이터 한국어대화요약](https://aihub.or.kr/aidata/30714)
- [개방데이터 감성 대화 말뭉치](https://aihub.or.kr/aidata/7978)
- [개방데이터 한국어 음성](https://aihub.or.kr/aidata/105)
- [개방데이터 한국어 SNS](https://aihub.or.kr/aidata/30718)
### [세종 말뭉치](https://ithub.korean.go.kr/)
|
{"language": "ko"}
|
text2text-generation
|
cosmoquester/bart-ko-mini
|
[
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
|
Pretrained BART in Korean
=========================
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by TPU Research Cloud program.
The script which is used to pre-train model is here.
When you use the reference API, you must wrap the sentence with '[BOS]' and '[EOS]' like below example.
You can also test mask filling performance using '[MASK]' token like this.
Benchmark
---------
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
* The performance was measured using the notebooks here with colab.
Used Datasets
-------------
### 모두의 말뭉치
* 일상 대화 말뭉치 2020
* 구어 말뭉치
* 문어 말뭉치
* 신문 말뭉치
### AIhub
* 개방데이터 전문분야말뭉치
* 개방데이터 한국어대화요약
* 개방데이터 감성 대화 말뭉치
* 개방데이터 한국어 음성
* 개방데이터 한국어 SNS
### 세종 말뭉치
|
[
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
[
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
[
43,
32,
49,
6
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS### 세종 말뭉치"
] |
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] |
null | null |
transformers
|
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train model is [here](https://github.com/cosmoquester/transformers-bart-pretrain).
When you use the reference API, you must wrap the sentence with `[BOS]` and `[EOS]` like below example.
```
[BOS] 안녕하세요? 반가워요~~ [EOS]
```
You can also test mask filling performance using `[MASK]` token like this.
```
[BOS] [MASK] 먹었어? [EOS]
```
## Benchmark
<style>
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
</style>
<table>
<tr>
<th>Dataset</th>
<td>KLUE NLI dev</th>
<td>NSMC test</td>
<td>QuestionPair test</td>
<td colspan="2">KLUE TC dev</td>
<td colspan="3">KLUE STS dev</td>
<td colspan="3">KorSTS dev</td>
<td colspan="2">HateSpeech dev</td>
</tr>
<tr>
<th>Metric</th>
<!-- KLUE NLI -->
<td>Acc</th>
<!-- NSMC -->
<td>Acc</td>
<!-- QuestionPair -->
<td>Acc</td>
<!-- KLUE TC -->
<td>Acc</td>
<td>F1</td>
<!-- KLUE STS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- KorSTS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- HateSpeech -->
<td>Bias Acc</td>
<td>Hate Acc</td>
</tr>
<tr>
<th>Score</th>
<!-- KLUE NLI -->
<td>0.639</th>
<!-- NSMC -->
<td>0.8721</td>
<!-- QuestionPair -->
<td>0.905</td>
<!-- KLUE TC -->
<td>0.8551</td>
<td>0.8515</td>
<!-- KLUE STS -->
<td>0.7406</td>
<td>0.7593</td>
<td>0.7551</td>
<!-- KorSTS -->
<td>0.7897</td>
<td>0.7269</td>
<td>0.7037</td>
<!-- HateSpeech -->
<td>0.8068</td>
<td>0.5966</td>
</tr>
</table>
- The performance was measured using [the notebooks here](https://github.com/cosmoquester/transformers-bart-finetune) with colab.
## Used Datasets
### [모두의 말뭉치](https://corpus.korean.go.kr/)
- 일상 대화 말뭉치 2020
- 구어 말뭉치
- 문어 말뭉치
- 신문 말뭉치
### AIhub
- [개방데이터 전문분야말뭉치](https://aihub.or.kr/aidata/30717)
- [개방데이터 한국어대화요약](https://aihub.or.kr/aidata/30714)
- [개방데이터 감성 대화 말뭉치](https://aihub.or.kr/aidata/7978)
- [개방데이터 한국어 음성](https://aihub.or.kr/aidata/105)
- [개방데이터 한국어 SNS](https://aihub.or.kr/aidata/30718)
### [세종 말뭉치](https://ithub.korean.go.kr/)
|
{"language": "ko"}
|
text2text-generation
|
cosmoquester/bart-ko-small
|
[
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us
|
Pretrained BART in Korean
=========================
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by TPU Research Cloud program.
The script which is used to pre-train model is here.
When you use the reference API, you must wrap the sentence with '[BOS]' and '[EOS]' like below example.
You can also test mask filling performance using '[MASK]' token like this.
Benchmark
---------
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
* The performance was measured using the notebooks here with colab.
Used Datasets
-------------
### 모두의 말뭉치
* 일상 대화 말뭉치 2020
* 구어 말뭉치
* 문어 말뭉치
* 신문 말뭉치
### AIhub
* 개방데이터 전문분야말뭉치
* 개방데이터 한국어대화요약
* 개방데이터 감성 대화 말뭉치
* 개방데이터 한국어 음성
* 개방데이터 한국어 SNS
### 세종 말뭉치
|
[
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
[
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치",
"### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS",
"### 세종 말뭉치"
] |
[
43,
32,
49,
6
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bart #text2text-generation #ko #autotrain_compatible #endpoints_compatible #region-us \n### 모두의 말뭉치\n\n\n* 일상 대화 말뭉치 2020\n* 구어 말뭉치\n* 문어 말뭉치\n* 신문 말뭉치### AIhub\n\n\n* 개방데이터 전문분야말뭉치\n* 개방데이터 한국어대화요약\n* 개방데이터 감성 대화 말뭉치\n* 개방데이터 한국어 음성\n* 개방데이터 한국어 SNS### 세종 말뭉치"
] |
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0.0711347684264183,
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-eo
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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", "eo", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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():
logits = 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])
```
## Evaluation
The model can be evaluated as follows on the Esperanto test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import jiwer
def chunked_wer(targets, predictions, chunk_size=None):
if chunk_size is None: return jiwer.wer(targets, predictions)
start = 0
end = chunk_size
H, S, D, I = 0, 0, 0, 0
while start < len(targets):
chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
H = H + chunk_metrics["hits"]
S = S + chunk_metrics["substitutions"]
D = D + chunk_metrics["deletions"]
I = I + chunk_metrics["insertions"]
start += chunk_size
end += chunk_size
return float(S + D + I) / float(H + S + D)
test_dataset = load_dataset("common_voice", "eo", 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("cpierse/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=2000)))
```
**Test Result**: 12.31 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
{"language": "eo", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Esperanto by Charles Pierse", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice eo", "type": "common_voice", "args": "eo"}, "metrics": [{"type": "wer", "value": 12.31, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cpierse/wav2vec2-large-xlsr-53-esperanto
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eo",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"eo"
] |
TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-eo
Fine-tuned facebook/wav2vec2-large-xlsr-53 on esperanto using the Common Voice dataset.
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:
## Evaluation
The model can be evaluated as follows on the Esperanto test data of Common Voice.
Test Result: 12.31 %
## Training
The Common Voice 'train', 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-eo \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on esperanto using the Common Voice dataset. \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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Esperanto test data of Common Voice. \n\n\n\n\nTest Result: 12.31 %",
"## Training\n\nThe Common Voice 'train', 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-eo \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on esperanto using the Common Voice dataset. \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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Esperanto test data of Common Voice. \n\n\n\n\nTest Result: 12.31 %",
"## Training\n\nThe Common Voice 'train', 'validation' datasets were used for training."
] |
[
89,
63,
20,
27,
23
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eo #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Wav2Vec2-Large-XLSR-53-eo \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on esperanto using the Common Voice dataset. \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:## Evaluation\n\nThe model can be evaluated as follows on the Esperanto test data of Common Voice. \n\n\n\n\nTest Result: 12.31 %## Training\n\nThe Common Voice 'train', 'validation' datasets were used for training."
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Irish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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", "ga-IE", split="test[:2%]") #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.
processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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():
logits = 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])
```
## Evaluation
The model can be evaluated as follows on the Irish test data of Common Voice.
```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", "ga-IE", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return 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**: 43.06 %
|
{"language": "ga-IE", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "cpierse/wav2vec2-large-xlsr-53-irish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ga-IE", "type": "common_voice", "args": "ga-IE"}, "metrics": [{"type": "wer", "value": 43.06, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cpierse/wav2vec2-large-xlsr-53-irish
|
[
"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
|
[] |
[
"ga-IE"
] |
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-Irish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voice dataset.
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:
## Evaluation
The model can be evaluated as follows on the Irish test data of Common Voice.
Test Result: 43.06 %
|
[
"# Wav2Vec2-Large-XLSR-53-Irish \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voice dataset. \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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Irish test data of Common Voice. \n\n\n\nTest Result: 43.06 %"
] |
[
"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-Irish \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voice dataset. \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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Irish test data of Common Voice. \n\n\n\nTest Result: 43.06 %"
] |
[
78,
64,
20,
28
] |
[
"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-Irish \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Irish using the Common Voice dataset. \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:## Evaluation\n\nThe model can be evaluated as follows on the Irish test data of Common Voice. \n\n\n\nTest Result: 43.06 %"
] |
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] |
null | null |
transformers
|
# Named Entity Recognition based on FERNET-CC_sk
This model is a fine-tuned version of [fav-kky/FERNET-CC_sk](https://huggingface.co/fav-kky/FERNET-CC_sk) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1763
- Precision: 0.9360
- Recall: 0.9472
- F1: 0.9416
- Accuracy: 0.9789
## Intended uses & limitation
Supported classes: LOCATION, PERSON, ORGANIZATION
```
from transformers import pipeline
ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1259 | 1.0 | 834 | 0.1095 | 0.8963 | 0.9182 | 0.9071 | 0.9697 |
| 0.071 | 2.0 | 1668 | 0.0974 | 0.9270 | 0.9357 | 0.9313 | 0.9762 |
| 0.0323 | 3.0 | 2502 | 0.1259 | 0.9257 | 0.9330 | 0.9293 | 0.9745 |
| 0.0175 | 4.0 | 3336 | 0.1347 | 0.9241 | 0.9360 | 0.9300 | 0.9756 |
| 0.0156 | 5.0 | 4170 | 0.1407 | 0.9337 | 0.9404 | 0.9370 | 0.9780 |
| 0.0062 | 6.0 | 5004 | 0.1522 | 0.9267 | 0.9410 | 0.9338 | 0.9774 |
| 0.0055 | 7.0 | 5838 | 0.1559 | 0.9322 | 0.9429 | 0.9375 | 0.9780 |
| 0.0024 | 8.0 | 6672 | 0.1733 | 0.9321 | 0.9438 | 0.9379 | 0.9779 |
| 0.0009 | 9.0 | 7506 | 0.1765 | 0.9347 | 0.9468 | 0.9407 | 0.9784 |
| 0.0002 | 10.0 | 8340 | 0.1763 | 0.9360 | 0.9472 | 0.9416 | 0.9789 |
### Framework versions
- Transformers 4.14.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"language": ["sk"], "license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "inference": false, "model-index": [{"name": "fernet-sk-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann sk", "type": "wikiann", "args": "sk"}, "metrics": [{"type": "precision", "value": 0.9359821760118826, "name": "Precision"}, {"type": "recall", "value": 0.9472378804960541, "name": "Recall"}, {"type": "f1", "value": 0.9415763914830033, "name": "F1"}, {"type": "accuracy", "value": 0.9789063466534702, "name": "Accuracy"}]}]}]}
|
token-classification
|
crabz/FERNET-CC_sk-ner
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sk"
] |
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #sk #dataset-wikiann #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #region-us
|
Named Entity Recognition based on FERNET-CC\_sk
===============================================
This model is a fine-tuned version of fav-kky/FERNET-CC\_sk on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1763
* Precision: 0.9360
* Recall: 0.9472
* F1: 0.9416
* Accuracy: 0.9789
Intended uses & limitation
--------------------------
Supported classes: LOCATION, PERSON, ORGANIZATION
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0
### Training results
### Framework versions
* Transformers 4.14.0.dev0
* Pytorch 1.10.0
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #sk #dataset-wikiann #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
61,
99,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #sk #dataset-wikiann #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0### Training results### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Named Entity Recognition based on bertoslav-limited
This model is a fine-tuned version of [crabz/bertoslav-limited](https://huggingface.co/crabz/bertoslav-limited) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Precision: 0.8986
- Recall: 0.9174
- F1: 0.9079
- Accuracy: 0.9700
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2953 | 1.0 | 834 | 0.1516 | 0.8413 | 0.8647 | 0.8529 | 0.9549 |
| 0.0975 | 2.0 | 1668 | 0.1304 | 0.8787 | 0.9056 | 0.8920 | 0.9658 |
| 0.0487 | 3.0 | 2502 | 0.1405 | 0.8916 | 0.8958 | 0.8937 | 0.9660 |
| 0.025 | 4.0 | 3336 | 0.1658 | 0.8850 | 0.9116 | 0.8981 | 0.9669 |
| 0.0161 | 5.0 | 4170 | 0.1739 | 0.8974 | 0.9127 | 0.9050 | 0.9693 |
| 0.0074 | 6.0 | 5004 | 0.1888 | 0.8900 | 0.9144 | 0.9020 | 0.9687 |
| 0.0051 | 7.0 | 5838 | 0.1996 | 0.8946 | 0.9145 | 0.9044 | 0.9693 |
| 0.0039 | 8.0 | 6672 | 0.2052 | 0.8993 | 0.9158 | 0.9075 | 0.9697 |
| 0.0024 | 9.0 | 7506 | 0.2112 | 0.8946 | 0.9171 | 0.9057 | 0.9696 |
| 0.0018 | 10.0 | 8340 | 0.2119 | 0.8986 | 0.9174 | 0.9079 | 0.9700 |
### Framework versions
- Transformers 4.14.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"language": ["sk"], "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "inference": false, "model-index": [{"name": "bertoslav-limited-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann sk", "type": "wikiann", "args": "sk"}, "metrics": [{"type": "precision", "value": 0.8985571260306242, "name": "Precision"}, {"type": "recall", "value": 0.9173994738819993, "name": "Recall"}, {"type": "f1", "value": 0.9078805459481573, "name": "F1"}, {"type": "accuracy", "value": 0.9700235061239639, "name": "Accuracy"}]}]}]}
|
token-classification
|
crabz/bertoslav-limited-ner
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"model-index",
"autotrain_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sk"
] |
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #sk #dataset-wikiann #model-index #autotrain_compatible #region-us
|
Named Entity Recognition based on bertoslav-limited
===================================================
This model is a fine-tuned version of crabz/bertoslav-limited on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2119
* Precision: 0.8986
* Recall: 0.9174
* F1: 0.9079
* Accuracy: 0.9700
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0
### Training results
### Framework versions
* Transformers 4.14.0.dev0
* Pytorch 1.10.0
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #sk #dataset-wikiann #model-index #autotrain_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
50,
99,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #sk #dataset-wikiann #model-index #autotrain_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0### Training results### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Named Entity Recognition based on SlovakBERT
This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1600
- Precision: 0.9327
- Recall: 0.9470
- F1: 0.9398
- Accuracy: 0.9785
## Intended uses & limitations
Supported classes: LOCATION, PERSON, ORGANIZATION
```
from transformers import pipeline
ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)
```
with `displaCy`:
```
import spacy
from spacy import displacy
ner_map = {0: '0', 1: 'B-OSOBA', 2: 'I-OSOBA', 3: 'B-ORGANIZÁCIA', 4: 'I-ORGANIZÁCIA', 5: 'B-LOKALITA', 6: 'I-LOKALITA'}
entities = []
for i in range(len(classifications)):
if classifications[i]['entity'] != 0:
if ner_map[classifications[i]['entity']][0] == 'B':
j = i + 1
while j < len(classifications) and ner_map[classifications[j]['entity']][0] == 'I':
j += 1
entities.append((ner_map[classifications[i]['entity']].split('-')[1], classifications[i]['start'],
classifications[j - 1]['end']))
nlp = spacy.blank("en") # it should work with any language
doc = nlp(input_sentence)
ents = []
for ee in entities:
ents.append(doc.char_span(ee[1], ee[2], ee[0]))
doc.ents = ents
options = {"ents": ["OSOBA", "ORGANIZÁCIA", "LOKALITA"],
"colors": {"OSOBA": "lightblue", "ORGANIZÁCIA": "lightcoral", "LOKALITA": "lightgreen"}}
displacy_html = displacy.render(doc, style="ent", options=options)
```
<div class="entities" style="line-height: 2.5; direction: ltr">Minister financií a líder mandátovo najsilnejšieho hnutia
<mark class="entity" style="background: lightcoral; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
OĽaNO
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORGANIZÁCIA</span>
</mark>
<mark class="entity" style="background: lightblue; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Igor Matovič
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">OSOBA</span>
</mark>
upozorňuje, že následky tretej vlny budú na
<mark class="entity" style="background: lightgreen; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
Slovensku
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOKALITA</span>
</mark>
veľmi veľké.</div>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2342 | 1.0 | 625 | 0.1233 | 0.8891 | 0.9076 | 0.8982 | 0.9667 |
| 0.1114 | 2.0 | 1250 | 0.1079 | 0.9118 | 0.9269 | 0.9193 | 0.9725 |
| 0.0817 | 3.0 | 1875 | 0.1093 | 0.9173 | 0.9315 | 0.9243 | 0.9747 |
| 0.0438 | 4.0 | 2500 | 0.1076 | 0.9188 | 0.9353 | 0.9270 | 0.9743 |
| 0.028 | 5.0 | 3125 | 0.1230 | 0.9143 | 0.9387 | 0.9264 | 0.9744 |
| 0.0256 | 6.0 | 3750 | 0.1204 | 0.9246 | 0.9423 | 0.9334 | 0.9765 |
| 0.018 | 7.0 | 4375 | 0.1332 | 0.9292 | 0.9416 | 0.9353 | 0.9770 |
| 0.0107 | 8.0 | 5000 | 0.1339 | 0.9280 | 0.9427 | 0.9353 | 0.9769 |
| 0.0079 | 9.0 | 5625 | 0.1368 | 0.9326 | 0.9442 | 0.9383 | 0.9785 |
| 0.0065 | 10.0 | 6250 | 0.1490 | 0.9284 | 0.9445 | 0.9364 | 0.9772 |
| 0.0061 | 11.0 | 6875 | 0.1566 | 0.9328 | 0.9433 | 0.9380 | 0.9778 |
| 0.0031 | 12.0 | 7500 | 0.1555 | 0.9339 | 0.9473 | 0.9406 | 0.9787 |
| 0.0024 | 13.0 | 8125 | 0.1548 | 0.9349 | 0.9462 | 0.9405 | 0.9787 |
| 0.0015 | 14.0 | 8750 | 0.1562 | 0.9330 | 0.9469 | 0.9399 | 0.9788 |
| 0.0013 | 15.0 | 9375 | 0.1600 | 0.9327 | 0.9470 | 0.9398 | 0.9785 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"language": ["sk"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "inference": false, "widget": [{"text": "Zuzana \u010caputov\u00e1 sa narodila 21. j\u00fana 1973 v Bratislave.", "example_title": "Named Entity Recognition"}], "base_model": "gerulata/slovakbert", "model-index": [{"name": "slovakbert-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "sk"}, "metrics": [{"type": "precision", "value": 0.9327115256495669, "name": "Precision"}, {"type": "recall", "value": 0.9470124013528749, "name": "Recall"}, {"type": "f1", "value": 0.9398075632132469, "name": "F1"}, {"type": "accuracy", "value": 0.9785228256835333, "name": "Accuracy"}]}]}]}
|
token-classification
|
crabz/slovakbert-ner
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"sk",
"dataset:wikiann",
"base_model:gerulata/slovakbert",
"license:mit",
"model-index",
"autotrain_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sk"
] |
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #sk #dataset-wikiann #base_model-gerulata/slovakbert #license-mit #model-index #autotrain_compatible #has_space #region-us
|
Named Entity Recognition based on SlovakBERT
============================================
This model is a fine-tuned version of gerulata/slovakbert on the Slovak wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1600
* Precision: 0.9327
* Recall: 0.9470
* F1: 0.9398
* Accuracy: 0.9785
Intended uses & limitations
---------------------------
Supported classes: LOCATION, PERSON, ORGANIZATION
with 'displaCy':
Minister financií a líder mandátovo najsilnejšieho hnutia
OĽaNO
ORGANIZÁCIA
Igor Matovič
OSOBA
upozorňuje, že následky tretej vlny budú na
Slovensku
LOKALITA
veľmi veľké.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15.0
### Training results
### Framework versions
* Transformers 4.13.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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: 15.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #sk #dataset-wikiann #base_model-gerulata/slovakbert #license-mit #model-index #autotrain_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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: 15.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
70,
99,
4,
36
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #sk #dataset-wikiann #base_model-gerulata/slovakbert #license-mit #model-index #autotrain_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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: 15.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Frisian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Frisian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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", "fy-NL", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("crang/wav2vec2-large-xlsr-53-frisian")
model = Wav2Vec2ForCTC.from_pretrained("crang/wav2vec2-large-xlsr-53-frisian")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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():
logits = 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])
```
## Evaluation
The model can be evaluated as follows on the Frisian test data of Common Voice.
```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", "fy-NL", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("crang/wav2vec2-large-xlsr-53-frisian")
model = Wav2Vec2ForCTC.from_pretrained("crang/wav2vec2-large-xlsr-53-frisian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\u2013\u2014\;\:\"\\%\\\]'
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return 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**: 19.11 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "fy-NL", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Frisian XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fy-NL", "type": "common_voice", "args": "fy-NL"}, "metrics": [{"type": "wer", "value": 19.11, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
crang/wav2vec2-large-xlsr-53-frisian
|
[
"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
|
[] |
[
"fy-NL"
] |
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-Frisian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common Voice dataset.
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:
## Evaluation
The model can be evaluated as follows on the Frisian test data of Common Voice.
Test Result: 19.11 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common Voice dataset.\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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Frisian test data of Common Voice.\n\n\n\n\nTest Result: 19.11 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"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-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common Voice dataset.\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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Frisian test data of Common Voice.\n\n\n\n\nTest Result: 19.11 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
78,
66,
20,
28,
23
] |
[
"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-Frisian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Frisian using the Common Voice dataset.\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:## Evaluation\n\nThe model can be evaluated as follows on the Frisian test data of Common Voice.\n\n\n\n\nTest Result: 19.11 %## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tatar using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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", "tt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar")
model = Wav2Vec2ForCTC.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar")
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):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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():
logits = 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])
```
## Evaluation
The model can be evaluated as follows on the Tatar test data of Common Voice.
```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", "tt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar")
model = Wav2Vec2ForCTC.from_pretrained("crang/wav2vec2-large-xlsr-53-tatar")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\u2013\u2014\;\:\"\\%\\\]'
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return 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):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return 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**: 30.93 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "tt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Tatar XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tt", "type": "common_voice", "args": "tt"}, "metrics": [{"type": "wer", "value": 30.93, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
crang/wav2vec2-large-xlsr-53-tatar
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tt"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.
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:
## Evaluation
The model can be evaluated as follows on the Tatar test data of Common Voice.
Test Result: 30.93 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Tatar test data of Common Voice.\n\n\n\n\nTest Result: 30.93 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\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:",
"## Evaluation\n\nThe model can be evaluated as follows on the Tatar test data of Common Voice.\n\n\n\n\nTest Result: 30.93 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
80,
65,
20,
29,
23
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\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:## Evaluation\n\nThe model can be evaluated as follows on the Tatar test data of Common Voice.\n\n\n\n\nTest Result: 30.93 %## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "bg", "mk"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Bg
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"bg",
"mk",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"bg",
"mk"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #bg #mk #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #bg #mk #license-mit #endpoints_compatible #region-us \n"
] |
[
41
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #bg #mk #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
4. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "cs"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Cs
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"cs",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"cs"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #cs #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
4. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #license-mit #endpoints_compatible #region-us \n"
] |
[
39
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
6. NER-UK (LOC, MISC, ORG, PER)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "ru", "bg", "mk", "uk", "fi"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Cyrillic1
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
6. NER-UK (LOC, MISC, ORG, PER)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
6. NER-UK (LOC, MISC, ORG, PER)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "ru", "bg", "mk", "uk", "fi"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Cyrillic2
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"ru",
"bg",
"mk",
"uk",
"fi"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
6. NER-UK (LOC, MISC, ORG, PER)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #ru #bg #mk #uk #fi #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "cs", "pl", "sl", "fi"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Latin1
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"cs",
"pl",
"sl",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"cs",
"pl",
"sl",
"fi"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "cs", "pl", "sl", "fi"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Latin2
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"cs",
"pl",
"sl",
"fi",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"cs",
"pl",
"sl",
"fi"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SlavNER 17 (LOC, MISC, ORG, PER)
4. SSJ500k (LOC, MISC, ORG, PER)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. CNEC (LOC, ORG, MEDIA, ART, PER, TIME)
7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #cs #pl #sl #fi #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a Polish NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on Polish BERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. NKJP (DATE, GEOPOLIT, LOC, ORG, PER, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["pl"], "license": "mit", "tags": ["polish_bert", "ner"]}
| null |
creat89/NER_FEDA_Pl
|
[
"transformers",
"pytorch",
"bert",
"polish_bert",
"ner",
"pl",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #bert #polish_bert #ner #pl #license-mit #endpoints_compatible #region-us
|
This is a Polish NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on Polish BERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
5. KPWr (EVT, LOC, ORG, PER, PRO)
6. NKJP (DATE, GEOPOLIT, LOC, ORG, PER, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #polish_bert #ner #pl #license-mit #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #bert #polish_bert #ner #pl #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a Russian NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on RuBERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["ru"], "license": "mit", "tags": ["rubert", "ner"]}
| null |
creat89/NER_FEDA_Ru
|
[
"transformers",
"pytorch",
"bert",
"rubert",
"ner",
"ru",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #bert #rubert #ner #ru #license-mit #endpoints_compatible #region-us
|
This is a Russian NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on RuBERT and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG)
5. FactRuEval (LOC, ORG, PER)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #rubert #ner #ru #license-mit #endpoints_compatible #region-us \n"
] |
[
35
] |
[
"passage: TAGS\n#transformers #pytorch #bert #rubert #ner #ru #license-mit #endpoints_compatible #region-us \n"
] |
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