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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CEBaB/gpt2.CEBaB.causalm.food__service.2-class.exclusive.seed_46 | 1168bf599b19d00eaa8e3c9c3713470a62192712 | 2022-05-24T10:04:53.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.food__service.2-class.exclusive.seed_46 | 2 | null | transformers | 26,100 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.noise__food.2-class.exclusive.seed_42 | 1caed6e87b767b532fb90e305377fb98959427f4 | 2022-05-24T10:04:55.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.noise__food.2-class.exclusive.seed_42 | 2 | null | transformers | 26,101 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.noise__food.2-class.exclusive.seed_43 | 99d4815b621dd252875116bb77310fbeb9ce7741 | 2022-05-24T10:04:57.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.noise__food.2-class.exclusive.seed_43 | 2 | null | transformers | 26,102 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.noise__food.2-class.exclusive.seed_46 | 8619a5f34e20a3acd198f5125e0276adb9501d78 | 2022-05-24T10:05:03.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.noise__food.2-class.exclusive.seed_46 | 2 | null | transformers | 26,103 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.2-class.exclusive.seed_42 | a9117264cc2fd479399d80e9934784e04e9d922d | 2022-05-24T10:05:05.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.2-class.exclusive.seed_42 | 2 | null | transformers | 26,104 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.2-class.exclusive.seed_45 | f20af62802888bdb5e9c84d62ec8e8c063e9429f | 2022-05-24T10:05:11.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.2-class.exclusive.seed_45 | 2 | null | transformers | 26,105 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.3-class.exclusive.seed_42 | c705274c20f260ee00d0d6241d58fa1196986a16 | 2022-05-24T10:08:24.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.3-class.exclusive.seed_42 | 2 | null | transformers | 26,106 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.3-class.exclusive.seed_43 | 1fb1fd17929f2bc85a7bca3d99dbec86c57c3bf3 | 2022-05-24T10:08:26.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.3-class.exclusive.seed_43 | 2 | null | transformers | 26,107 | Entry not found |
CEBaB/bert-base-uncased.CEBaB.causalm.noise__food.5-class.exclusive.seed_46 | dc11f75131de7c2a4f69f4981e95cb244c2ccee7 | 2022-05-24T10:10:02.000Z | [
"pytorch",
"bert_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB.causalm.noise__food.5-class.exclusive.seed_46 | 2 | null | transformers | 26,108 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_42 | b5e7f51550ca3102ab17999c581c661184e6299c | 2022-05-24T10:11:45.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_42 | 2 | null | transformers | 26,109 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_43 | 9b3d7ee19a8d991fb453c81852f4844d4e7209bb | 2022-05-24T10:11:47.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_43 | 2 | null | transformers | 26,110 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_44 | fd188d31a4d3a1ea04666c59d0734ef93518ef0d | 2022-05-24T10:11:49.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_44 | 2 | null | transformers | 26,111 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_45 | 65040dacf00db2a5fe1b59359e1cd3ff2449c776 | 2022-05-24T10:11:51.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_45 | 2 | null | transformers | 26,112 | Entry not found |
CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_46 | fdde94f1ca1cecb5811ed17b9726cc26402155b0 | 2022-05-24T10:11:53.000Z | [
"pytorch",
"gpt2_causalm",
"transformers"
] | null | false | CEBaB | null | CEBaB/gpt2.CEBaB.causalm.service__food.5-class.exclusive.seed_46 | 2 | null | transformers | 26,113 | Entry not found |
stephenleejm/T5_yoda_translator | 75c5901f0ca30e934d941d666edeb3b67d51f037 | 2022-06-06T07:01:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | stephenleejm | null | stephenleejm/T5_yoda_translator | 2 | null | transformers | 26,114 | # Introduction
This model translate between Yoda-ish to English and vice versa. It makes use of the [T5-base](https://huggingface.co/t5-base) model and finetuning.
Basically it trains for 2 tasks using the same dataset. In Yoda-ish to English, trains
# Dataset
For this first version of the model I used a small sample of 20 Yoda quotes for training. I am in the midst of collecting more samples for training.
# Usage
**Input**
For Yoda-ish to English, you can use the prefix "y_to_e: text" to pass in as the input.
For English to Yodaish you can use the prefix "e_to_y: text"
**Output**
The translated sentence.
E.g
e_to_y: I am sick of you => Sick of you, I am
# Spaces
To try this model you can access it [here](https://huggingface.co/spaces/stephenleejm/yoda_translator) |
hamidov02/wav2vec2-large-xls-r-53h-turkish-colab | 3c74121ff05f55dd9867dd58ca9d8294778addb3 | 2022-05-24T08:50:22.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | hamidov02 | null | hamidov02/wav2vec2-large-xls-r-53h-turkish-colab | 2 | null | transformers | 26,115 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-53h-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-53h-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4135
- Wer: 0.3247
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 32
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.4875 | 0.92 | 100 | 3.5328 | 1.0 |
| 3.1866 | 1.83 | 200 | 3.0955 | 1.0 |
| 2.027 | 2.75 | 300 | 0.9002 | 0.7685 |
| 0.7285 | 3.67 | 400 | 0.6279 | 0.6693 |
| 0.4693 | 4.59 | 500 | 0.5672 | 0.5643 |
| 0.3615 | 5.5 | 600 | 0.4995 | 0.5094 |
| 0.2846 | 6.42 | 700 | 0.4561 | 0.4797 |
| 0.2253 | 7.34 | 800 | 0.4742 | 0.4675 |
| 0.2004 | 8.26 | 900 | 0.4462 | 0.4345 |
| 0.173 | 9.17 | 1000 | 0.4688 | 0.4333 |
| 0.1547 | 10.09 | 1100 | 0.4429 | 0.4206 |
| 0.1444 | 11.01 | 1200 | 0.4662 | 0.4144 |
| 0.1274 | 11.93 | 1300 | 0.4675 | 0.4213 |
| 0.1164 | 12.84 | 1400 | 0.4947 | 0.4073 |
| 0.1081 | 13.76 | 1500 | 0.4223 | 0.3915 |
| 0.1025 | 14.68 | 1600 | 0.4493 | 0.3912 |
| 0.0944 | 15.6 | 1700 | 0.4527 | 0.3848 |
| 0.0943 | 16.51 | 1800 | 0.4288 | 0.3810 |
| 0.0885 | 17.43 | 1900 | 0.4313 | 0.3670 |
| 0.0781 | 18.35 | 2000 | 0.4729 | 0.3790 |
| 0.0828 | 19.27 | 2100 | 0.4560 | 0.3651 |
| 0.0753 | 20.18 | 2200 | 0.4478 | 0.3599 |
| 0.0702 | 21.1 | 2300 | 0.4518 | 0.3595 |
| 0.0666 | 22.02 | 2400 | 0.4080 | 0.3489 |
| 0.0661 | 22.94 | 2500 | 0.4414 | 0.3507 |
| 0.0607 | 23.85 | 2600 | 0.4209 | 0.3538 |
| 0.058 | 24.77 | 2700 | 0.4302 | 0.3382 |
| 0.0596 | 25.69 | 2800 | 0.3939 | 0.3328 |
| 0.052 | 26.61 | 2900 | 0.4374 | 0.3311 |
| 0.0473 | 27.52 | 3000 | 0.4406 | 0.3363 |
| 0.0483 | 28.44 | 3100 | 0.4272 | 0.3286 |
| 0.049 | 29.36 | 3200 | 0.4189 | 0.3257 |
| 0.0433 | 30.28 | 3300 | 0.4242 | 0.3229 |
| 0.0438 | 31.19 | 3400 | 0.4135 | 0.3247 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
himanshubeniwal/bert_sst_ft | b3116ad41cdf552c4865dc89576f528f3409b6dc | 2022-05-24T05:58:34.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | himanshubeniwal | null | himanshubeniwal/bert_sst_ft | 2 | null | transformers | 26,116 | Entry not found |
KoichiYasuoka/deberta-base-japanese-upos | 57e025f218c419ae19f8d91ee956b12776fea31a | 2022-05-24T08:16:37.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"ja",
"dataset:universal_dependencies",
"transformers",
"japanese",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/deberta-base-japanese-upos | 2 | null | transformers | 26,117 | ---
language:
- "ja"
tags:
- "japanese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# deberta-base-japanese-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-aozora). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-japanese-upos")
s="国境の長いトンネルを抜けると雪国であった。"
t=tokenizer.tokenize(s)
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
print(list(zip(t,p)))
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-base-japanese-upos")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
PontifexMaximus/opus-mt-de-en-finetuned-de-to-en | 80af21dcc659d20bdaaa7545c38e918befd2bc2b | 2022-05-24T11:38:56.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt14",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | PontifexMaximus | null | PontifexMaximus/opus-mt-de-en-finetuned-de-to-en | 2 | null | transformers | 26,118 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt14
model-index:
- name: opus-mt-de-en-finetuned-de-to-en
results: []
---
<!-- 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. -->
# opus-mt-de-en-finetuned-de-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt14 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3411
- eval_bleu: 32.4395
- eval_gen_len: 29.6925
- eval_runtime: 2250.0489
- eval_samples_per_second: 19.998
- eval_steps_per_second: 0.625
- epoch: 3.0
- step: 4221
## 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-06
- 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: 11
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.19.2
- Pytorch 1.7.1+cu110
- Datasets 2.2.2
- Tokenizers 0.12.1
|
AswiN037/sentence-t-roberta-large-wechsel-tamil | 7dde5bdc7f43827907a98fe4c5aea4c85b4c5074 | 2022-05-25T08:55:45.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | AswiN037 | null | AswiN037/sentence-t-roberta-large-wechsel-tamil | 2 | 1 | sentence-transformers | 26,119 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sent-Roberta-wechsel-tamil
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
masoumehb/wav2vec2-large-xlsr-persian-v3 | 918f655ca45ef4b729b496288139114a3fdf2b1a | 2022-05-24T13:55:20.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | masoumehb | null | masoumehb/wav2vec2-large-xlsr-persian-v3 | 2 | 0 | transformers | 26,120 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-persian-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-persian-v3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.13.3
- Tokenizers 0.10.3
|
kimcando/test3 | e5974ce3ec6611a701e5fc4a56467b1639771cd6 | 2022-05-24T13:12:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | kimcando | null | kimcando/test3 | 2 | null | transformers | 26,121 | Entry not found |
hamidov02/wav2vec2-large-xls-hun-53h-colab | 66afc1b2a9cc64f03b719eff47783cd787c23f4e | 2022-05-24T19:38:50.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | hamidov02 | null | hamidov02/wav2vec2-large-xls-hun-53h-colab | 2 | null | transformers | 26,122 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-hun-53h-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-hun-53h-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6027
- Wer: 0.4618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 23
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 13.4225 | 0.67 | 100 | 3.7750 | 1.0 |
| 3.4121 | 1.34 | 200 | 3.3166 | 1.0 |
| 3.2263 | 2.01 | 300 | 3.1403 | 1.0 |
| 3.0038 | 2.68 | 400 | 2.2474 | 0.9990 |
| 1.2243 | 3.35 | 500 | 0.8174 | 0.7666 |
| 0.6368 | 4.03 | 600 | 0.6306 | 0.6633 |
| 0.4426 | 4.7 | 700 | 0.6151 | 0.6648 |
| 0.3821 | 5.37 | 800 | 0.5765 | 0.6138 |
| 0.3337 | 6.04 | 900 | 0.5522 | 0.5785 |
| 0.2832 | 6.71 | 1000 | 0.5822 | 0.5691 |
| 0.2485 | 7.38 | 1100 | 0.5626 | 0.5449 |
| 0.2335 | 8.05 | 1200 | 0.5866 | 0.5662 |
| 0.2031 | 8.72 | 1300 | 0.5574 | 0.5420 |
| 0.1925 | 9.39 | 1400 | 0.5572 | 0.5297 |
| 0.1793 | 10.07 | 1500 | 0.5878 | 0.5185 |
| 0.1652 | 10.74 | 1600 | 0.6173 | 0.5243 |
| 0.1663 | 11.41 | 1700 | 0.5807 | 0.5133 |
| 0.1544 | 12.08 | 1800 | 0.5979 | 0.5154 |
| 0.148 | 12.75 | 1900 | 0.5545 | 0.4986 |
| 0.138 | 13.42 | 2000 | 0.5798 | 0.4947 |
| 0.1353 | 14.09 | 2100 | 0.5670 | 0.5028 |
| 0.1283 | 14.76 | 2200 | 0.5862 | 0.4957 |
| 0.1271 | 15.43 | 2300 | 0.6009 | 0.4961 |
| 0.1108 | 16.11 | 2400 | 0.5873 | 0.4975 |
| 0.1182 | 16.78 | 2500 | 0.6013 | 0.4893 |
| 0.103 | 17.45 | 2600 | 0.6165 | 0.4898 |
| 0.1084 | 18.12 | 2700 | 0.6186 | 0.4838 |
| 0.1014 | 18.79 | 2800 | 0.6122 | 0.4767 |
| 0.1009 | 19.46 | 2900 | 0.5981 | 0.4793 |
| 0.1004 | 20.13 | 3000 | 0.6034 | 0.4770 |
| 0.0922 | 20.8 | 3100 | 0.6127 | 0.4663 |
| 0.09 | 21.47 | 3200 | 0.5967 | 0.4672 |
| 0.0893 | 22.15 | 3300 | 0.6051 | 0.4611 |
| 0.0817 | 22.82 | 3400 | 0.6027 | 0.4618 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
laituan245/rag-token-bart-base | bb99d38de82f700b6ee325010e2ac8980c998f29 | 2022-05-24T17:37:03.000Z | [
"pytorch",
"rag",
"transformers"
] | null | false | laituan245 | null | laituan245/rag-token-bart-base | 2 | null | transformers | 26,123 | This model is a non-finetuned RAG-Token model and was created as follows:
```python
from transformers import RagTokenizer, RagTokenForGeneration, AutoTokenizer
model = RagTokenForGeneration.from_pretrained_question_encoder_generator(
"facebook/dpr-question_encoder-single-nq-base",
"facebook/bart-base"
)
question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
model.save_pretrained("./")
tokenizer.save_pretrained("./")
```
|
ronanki/ml_use_13 | 8318fbdd41a7436abf23ae1aa5fcd5cb5ca39eb6 | 2022-05-24T17:43:08.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | ronanki | null | ronanki/ml_use_13 | 2 | null | sentence-transformers | 26,124 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# ronanki/ml_use_13
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ronanki/ml_use_13')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/ml_use_13)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8 with parameters:
```
{'batch_size': 4}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
laituan245/rag-sequence-bart-base | 1fba766a2b8b6e7bd58475afcd7b5f12c19bd205 | 2022-05-24T17:49:50.000Z | [
"pytorch",
"rag",
"transformers"
] | null | false | laituan245 | null | laituan245/rag-sequence-bart-base | 2 | null | transformers | 26,125 | This model is a non-finetuned RAG-Token model and was created as follows:
```python
from transformers import RagTokenizer, RagSequenceForGeneration, AutoTokenizer
model = RagSequenceForGeneration.from_pretrained_question_encoder_generator(
"facebook/dpr-question_encoder-single-nq-base",
"facebook/bart-base"
)
question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
model.save_pretrained("./")
tokenizer.save_pretrained("./")
```
|
castorini/afriteva_small | 6760505fb3977fb31c4e58050e6bb207d085fb48 | 2022-05-24T20:16:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"om",
"am",
"rw",
"rn",
"ha",
"ig",
"pcm",
"so",
"sw",
"ti",
"yo",
"multilingual",
"T5",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/afriteva_small | 2 | null | transformers | 26,126 | Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
- T5
---
# afriteva_small
## Model desription
AfriTeVa small is a sequence to sequence model pretrained on 10 African languages
## Languages
Afaan Oromoo(orm), Amharic(amh), Gahuza(gah), Hausa(hau), Igbo(igb), Nigerian Pidgin(pcm), Somali(som), Swahili(swa), Tigrinya(tig), Yoruba(yor)
### More information on the model, dataset:
### The model
- 64M parameters encoder-decoder architecture (T5-like)
- 6 layers, 8 attention heads and 512 token sequence length
### The dataset
- Multilingual: 10 African languages listed above
- 143 Million Tokens (1GB of text data)
- Tokenizer Vocabulary Size: 70,000 tokens
## Intended uses & limitations
`afriteva_small` is pre-trained model and primarily aimed at being fine-tuned on multilingual sequence-to-sequence tasks.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriteva_small")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("castorini/afriteva_small")
>>> src_text = "Ó hùn ọ́ láti di ara wa bí?"
>>> tgt_text = "Would you like to be?"
>>> model_inputs = tokenizer(src_text, return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
labels = tokenizer(tgt_text, return_tensors="pt").input_ids
>>> model(**model_inputs, labels=labels) # forward pass
```
## Training Procedure
For information on training procedures, please refer to the AfriTeVa [paper](#) or [repository](https://github.com/castorini/afriteva)
## BibTex entry and Citation info
coming soon ...
|
emilylearning/cond_ft_none_on_reddit__prcnt_20__test_run_False__xlm-roberta-base | 3f38d904b6697273597152a7ab7cd6fc1a7356c6 | 2022-05-25T07:53:16.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | emilylearning | null | emilylearning/cond_ft_none_on_reddit__prcnt_20__test_run_False__xlm-roberta-base | 2 | null | transformers | 26,127 | Entry not found |
logo-data-science/distilbert-finetuned | 52df852bf7005730e1318365d661a0ad7cb3ffd3 | 2022-05-25T05:55:44.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"license:gpl",
"autotrain_compatible"
] | question-answering | false | logo-data-science | null | logo-data-science/distilbert-finetuned | 2 | null | transformers | 26,128 | ---
license: gpl
---
|
emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base | 39d9e4e50aa97a534d2767fd1b5d3d7684403b63 | 2022-05-25T11:09:44.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | emilylearning | null | emilylearning/cond_ft_subreddit_on_reddit__prcnt_20__test_run_False__xlm-roberta-base | 2 | null | transformers | 26,129 | Entry not found |
OHenry/OHenry | 0100bcf7c643affd201c0a8abdb585762d9b5103 | 2022-05-25T09:16:44.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | OHenry | null | OHenry/OHenry | 2 | null | transformers | 26,130 | Entry not found |
jimypbr/bart-large-test | 9693a315d1b8b0d8cee8220e883647c8e8e8aa5e | 2022-05-25T12:02:26.000Z | [
"pytorch",
"bart",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | jimypbr | null | jimypbr/bart-large-test | 2 | null | transformers | 26,131 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: outputs
results: []
---
<!-- 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. -->
# outputs
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the cnn_dailymail 3.0.0 dataset.
## Model description
More information needed
## Intended uses & limitations
This is a work in progress. Please don't use these weights.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 256
- total_train_batch_size: 2048
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 2.0
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.2.1
- Tokenizers 0.12.1
|
thundaa/tape-fluorescence-prediction-RITA_s | ca4464f7e270be50782aefeaa3b11eed7fb29d50 | 2022-05-26T15:37:58.000Z | [
"pytorch",
"rita",
"text-classification",
"dataset:train",
"transformers",
"protein language model",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | thundaa | null | thundaa/tape-fluorescence-prediction-RITA_s | 2 | null | transformers | 26,132 | ---
license: apache-2.0
tags:
- protein language model
- generated_from_trainer
datasets:
- train
metrics:
- spearmanr
model-index:
- name: tape-fluorescence-prediction-RITA_s
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: cradle-bio/tape-fluorescence
type: train
metrics:
- name: Spearmanr
type: spearmanr
value: 0.2955275250425323
---
<!-- 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. -->
# tape-fluorescence-prediction-RITA_s
This model is a fine-tuned version of [lightonai/RITA_s](https://huggingface.co/lightonai/RITA_s) on the cradle-bio/tape-fluorescence dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5855
- Spearmanr: 0.2955
## 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: 32
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 4.3595 | 0.85 | 4 | 0.7057 | 0.0940 |
| 0.8654 | 1.85 | 8 | 0.6873 | 0.1280 |
| 0.8292 | 2.85 | 12 | 0.6835 | 0.2290 |
| 0.8212 | 3.85 | 16 | 0.6837 | 0.3110 |
| 0.8191 | 4.85 | 20 | 0.6799 | 0.3281 |
| 0.8137 | 5.85 | 24 | 0.6748 | 0.3277 |
| 0.8057 | 6.85 | 28 | 0.6592 | 0.3162 |
| 0.7769 | 7.85 | 32 | 0.6283 | 0.3065 |
| 0.7382 | 8.85 | 36 | 0.6103 | 0.2795 |
| 0.5991 | 9.85 | 40 | 0.5855 | 0.2955 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
shoubhik/electra_finetune | cbeeea14dcb68108a144096e5a44777d9c998ad4 | 2022-05-25T13:12:48.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | shoubhik | null | shoubhik/electra_finetune | 2 | null | transformers | 26,133 | Entry not found |
StephennFernandes/xls-r-300m-common_voice-ta | f5f84c8507998fa749822a4a325a6f03d2b5b039 | 2022-05-26T10:35:42.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | StephennFernandes | null | StephennFernandes/xls-r-300m-common_voice-ta | 2 | null | transformers | 26,134 | Entry not found |
xuio/roberta-sts | 2dae31cbe2d859b42011a6da878bc1e52fcb7b4e | 2022-05-26T01:38:59.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | xuio | null | xuio/roberta-sts | 2 | null | transformers | 26,135 | Entry not found |
emilylearning/cond_ft_none_on_reddit__prcnt_na__test_run_True__xlm-roberta-base | 74284501ffc87ae8d68f2290c4615b6b643423a3 | 2022-05-25T23:02:02.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | emilylearning | null | emilylearning/cond_ft_none_on_reddit__prcnt_na__test_run_True__xlm-roberta-base | 2 | null | transformers | 26,136 | Entry not found |
austin/t5_austin_large | a384ebc67b0ef86bd4482e34b2d185e05b702313 | 2022-06-02T20:29:54.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | austin | null | austin/t5_austin_large | 2 | null | transformers | 26,137 | Entry not found |
shoubhik/electra_freezed_9th_layer | 19da4484d87f750e1a4fe5bab359211eab54fb78 | 2022-05-26T11:58:03.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | shoubhik | null | shoubhik/electra_freezed_9th_layer | 2 | null | transformers | 26,138 | Entry not found |
aioxlabs/dvoice-wolof | 27e94ebd43ba590ef612f121c740661577adbea9 | 2022-05-28T08:22:16.000Z | [
"wav2vec2",
"feature-extraction",
"wo",
"dataset:commonvoice",
"speechbrain",
"CTC",
"pytorch",
"Transformer",
"license:apache-2.0",
"automatic-speech-recognition"
] | automatic-speech-recognition | false | aioxlabs | null | aioxlabs/dvoice-wolof | 2 | null | speechbrain | 26,139 | ---
language: "wo"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- pytorch
- speechbrain
- Transformer
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Wolof (No LM)
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Wolof dataset within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
| DVoice Release | Val. CER | Val. WER | Test CER | Test WER |
|:-------------:|:---------------------------:| -----:| -----:| -----:|
| v2.0 | 4.81 | 16.25 | 4.83 | 16.05 |
# Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions.
- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset.
The obtained final acoustic representation is given to the CTC greedy decoder.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
# Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers
```
Please notice that we encourage you to read the SpeechBrain tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
# Transcribing your own audio files (in Wolof)
```python
from speechbrain.pretrained import EncoderASR
asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-wolof", savedir="pretrained_models/asr-wav2vec2-dvoice-wol")
asr_model.transcribe_file('./the_path_to_your_audio_file')
```
# Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
# Training
To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice).
# Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# Referencing SpeechBrain
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
# About DVoice
DVoice is a community initiative that aims to provide Africa low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrived from social medias. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke.
For this project, AIOX Labs the SI2M Laboratory are joining forces to build the future of technologies together.
# About AIOX Labs
Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.
- He is at the service of the growth of groups, the optimization of processes or the improvement of the customer experience.
- AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods.
- Business ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
- A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications.
Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/)
# SI2M Laboratory
The Information Systems, Intelligent Systems and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network and System Security, Mathematical Modelling.
Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique)
# About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain
# Referencing SpeechBrain
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
# Acknowledgements
This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution. |
Gergoe/t5-small-booksum-finetuned-booksum-test | 974cb695fb8dd27402a671a1ddd0c9e3a7e56505 | 2022-05-26T21:41:22.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | summarization | false | Gergoe | null | Gergoe/t5-small-booksum-finetuned-booksum-test | 2 | null | transformers | 26,140 | ---
license: mit
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-booksum-finetuned-booksum-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-booksum-finetuned-booksum-test
This model is a fine-tuned version of [cnicu/t5-small-booksum](https://huggingface.co/cnicu/t5-small-booksum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2739
- Rouge1: 22.7829
- Rouge2: 4.8349
- Rougel: 18.2465
- Rougelsum: 19.2417
## 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: 5.6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.5123 | 1.0 | 8750 | 3.2816 | 21.7712 | 4.3046 | 17.4053 | 18.4707 |
| 3.2347 | 2.0 | 17500 | 3.2915 | 22.2938 | 4.7828 | 17.8567 | 18.9135 |
| 3.0892 | 3.0 | 26250 | 3.2568 | 22.4966 | 4.825 | 18.0344 | 19.1306 |
| 2.9837 | 4.0 | 35000 | 3.2952 | 22.6913 | 5.0322 | 18.176 | 19.2751 |
| 2.9028 | 5.0 | 43750 | 3.2626 | 22.3548 | 4.7521 | 17.8681 | 18.7815 |
| 2.8441 | 6.0 | 52500 | 3.2691 | 22.6279 | 4.932 | 18.1051 | 19.0763 |
| 2.8006 | 7.0 | 61250 | 3.2753 | 22.8911 | 4.8954 | 18.1204 | 19.1464 |
| 2.7742 | 8.0 | 70000 | 3.2739 | 22.7829 | 4.8349 | 18.2465 | 19.2417 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.7.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
actionpace/pegasus-samsum | 8416f5498613011ddddaf404e1bc611d915fc9a0 | 2022-05-26T19:11:21.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | actionpace | null | actionpace/pegasus-samsum | 2 | null | transformers | 26,141 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4841
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7073 | 0.54 | 500 | 1.4841 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kangela/Metaphor-FineTuned-BERT-5Epochs | 67c49e531b92755c91ff20ef462477519983b9a6 | 2022-05-31T08:20:36.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kangela | null | kangela/Metaphor-FineTuned-BERT-5Epochs | 2 | null | transformers | 26,142 | Entry not found |
castorini/mdpr-tied-pft-msmarco-ft-all | 7cf44df40bc9048163d2168b521783322b3eb531 | 2022-05-26T21:14:21.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/mdpr-tied-pft-msmarco-ft-all | 2 | null | transformers | 26,143 | The checkpoint is further fine-tuned based on the `castorini/mdpr-tied-pft-msmarco` checkpoint, on all the Mr. TyDi training data. |
nqcccccc/phobert-multilabel-post-classification | 83a2a410ec95b27007ecf78151d57f52ecb4c7d7 | 2022-05-27T07:13:41.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | nqcccccc | null | nqcccccc/phobert-multilabel-post-classification | 2 | null | transformers | 26,144 | Entry not found |
elisabethvonoswald/wav2vec2-large-xls-r-300m-27-05 | 27ba45388df4d2be32ce9fdd60f5c7ec8953e886 | 2022-05-27T13:38:55.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | elisabethvonoswald | null | elisabethvonoswald/wav2vec2-large-xls-r-300m-27-05 | 2 | null | transformers | 26,145 | Entry not found |
onewithnickelcoins/roberta-base-stars | 1a77d0b1fbe148559253be7d0496e0bbe6511707 | 2022-05-27T13:15:43.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | onewithnickelcoins | null | onewithnickelcoins/roberta-base-stars | 2 | null | transformers | 26,146 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-stars
results: []
---
<!-- 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. -->
# roberta-base-stars
This model is a fine-tuned version of [onewithnickelcoins/roberta-base-MLM](https://huggingface.co/onewithnickelcoins/roberta-base-MLM) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2914
- Accuracy: 0.6857
## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
SivilTaram/poet-sql | 6b5bb6d30df335b5b8e21650f8d9ff7187159f5d | 2022-05-27T13:29:58.000Z | [
"pytorch",
"bart",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | SivilTaram | null | SivilTaram/poet-sql | 2 | null | transformers | 26,147 | ---
license: apache-2.0
---
|
SivilTaram/poet-sql-digit | 148416e998dffe2e997776490745b2138fa99b6f | 2022-05-27T13:55:45.000Z | [
"pytorch",
"bart",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | SivilTaram | null | SivilTaram/poet-sql-digit | 2 | null | transformers | 26,148 | ---
license: apache-2.0
---
|
wuyue19871987/twitter-roberta-base-sentiment-finetuned | 59b9fa8b4e2f725a878f4a42880a91da6c24e6c8 | 2022-05-28T02:32:12.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | wuyue19871987 | null | wuyue19871987/twitter-roberta-base-sentiment-finetuned | 2 | null | transformers | 26,149 | Entry not found |
KoichiYasuoka/deberta-base-coptic | 287c0735a50ab79f1dae6b373849a55c2c97f000 | 2022-05-28T09:19:16.000Z | [
"pytorch",
"deberta-v2",
"fill-mask",
"cop",
"transformers",
"coptic",
"masked-lm",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | KoichiYasuoka | null | KoichiYasuoka/deberta-base-coptic | 2 | null | transformers | 26,150 | ---
language:
- "cop"
tags:
- "coptic"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# deberta-base-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `deberta-base-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-coptic-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-coptic")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-coptic")
```
|
JuanForeroNeme/ES_UC_MODELO_NPL_E3_V1 | db372a547b4d7d8cc06a8f79627d55767ccb8d18 | 2022-05-28T17:40:40.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | JuanForeroNeme | null | JuanForeroNeme/ES_UC_MODELO_NPL_E3_V1 | 2 | null | transformers | 26,151 | Entry not found |
JuanForeroNeme/ES_UC_MODELO_NPL_E3_V2 | 20f372a013a805a4ac0f47874696a9c1d341dcf9 | 2022-05-28T19:05:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | JuanForeroNeme | null | JuanForeroNeme/ES_UC_MODELO_NPL_E3_V2 | 2 | null | transformers | 26,152 | **ENTREGABLE 3**
* Magda Brigitte Baron
* Juan Guillermo Forero Neme
* Myriam Leguizamon Lopez
* Diego Alexander Maca Garcia |
GiordanoB/mbart-large-50-finetuned-summarization-V2 | 9c32cdb246c1c5686ec7f9002907a214d9b858eb | 2022-05-29T00:51:55.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | GiordanoB | null | GiordanoB/mbart-large-50-finetuned-summarization-V2 | 2 | null | transformers | 26,153 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mbart-large-50-finetuned-summarization-V2
results: []
---
<!-- 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. -->
# mbart-large-50-finetuned-summarization-V2
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9183
- Rouge1: 50.0118
- Rouge2: 31.3168
- Rougel: 37.6392
- Rougelsum: 45.2287
- Gen Len: 102.3571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 15 | 2.0228 | 51.9711 | 32.5963 | 39.9154 | 48.3431 | 134.6429 |
| No log | 2.0 | 30 | 1.9410 | 48.2977 | 30.5942 | 35.9761 | 43.7634 | 92.0714 |
| No log | 3.0 | 45 | 1.9183 | 50.0118 | 31.3168 | 37.6392 | 45.2287 | 102.3571 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
susghosh/bert-finetuned-squad | c6d467a0c29b97fcc2d3a0c5f23a06d68e3bca59 | 2022-05-29T14:55:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | susghosh | null | susghosh/bert-finetuned-squad | 2 | null | transformers | 26,154 | Entry not found |
hunkim/model1 | 381f9fa48dce03be650ce0595c16a873473f4217 | 2022-05-29T09:29:36.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | hunkim | null | hunkim/model1 | 2 | null | sentence-transformers | 26,155 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Sung/model1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Sung/model1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Sung/model1')
model = AutoModel.from_pretrained('Sung/model1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Sung/model1)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
shafin/distilbert-base-uncased-finetuned-cust-similarity-1 | e95c0c158c93a7f194d5b48b60317297953ce97a | 2022-05-29T09:49:25.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | shafin | null | shafin/distilbert-base-uncased-finetuned-cust-similarity-1 | 2 | 1 | sentence-transformers | 26,156 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# shafin/distilbert-base-uncased-finetuned-cust-similarity-1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('shafin/distilbert-base-uncased-finetuned-cust-similarity-1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=shafin/distilbert-base-uncased-finetuned-cust-similarity-1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4375 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 15,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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})
(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Dense({'in_features': 256, 'out_features': 32, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
hunkim/sentence-transformer-klue | 02257b1fdd65e61861456ece23700b1d14b79d32 | 2022-05-29T13:47:40.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | hunkim | null | hunkim/sentence-transformer-klue | 2 | null | sentence-transformers | 26,157 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Sung/sentence-transformer-klue
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Sung/sentence-transformer-klue')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Sung/sentence-transformer-klue')
model = AutoModel.from_pretrained('Sung/sentence-transformer-klue')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Sung/sentence-transformer-klue)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 365 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 146,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
pujaburman30/autotrain-hi_ner_xlmr_large-924630372 | 0ba257d6993841f7deb338c616ca2f911499b864 | 2022-05-29T13:44:19.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"unk",
"dataset:pujaburman30/autotrain-data-hi_ner_xlmr_large",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | pujaburman30 | null | pujaburman30/autotrain-hi_ner_xlmr_large-924630372 | 2 | null | transformers | 26,158 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- pujaburman30/autotrain-data-hi_ner_xlmr_large
co2_eq_emissions: 5.880084418778246
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 924630372
- CO2 Emissions (in grams): 5.880084418778246
## Validation Metrics
- Loss: 0.8206124901771545
- Accuracy: 0.7745009890307498
- Precision: 0.6042857142857143
- Recall: 0.6547987616099071
- F1: 0.6285289747399703
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/pujaburman30/autotrain-hi_ner_xlmr_large-924630372
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("pujaburman30/autotrain-hi_ner_xlmr_large-924630372", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("pujaburman30/autotrain-hi_ner_xlmr_large-924630372", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
xuio/sts-12ep | d47f823ff6ef4fc9b6304fe6bb25dfa9e3baf129 | 2022-05-29T15:00:01.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | xuio | null | xuio/sts-12ep | 2 | null | transformers | 26,159 | Entry not found |
chi0/kobart-dial-sum | c015a43853bf9fc5ebd2f630826f971c0fd96131 | 2022-05-29T15:19:28.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | chi0 | null | chi0/kobart-dial-sum | 2 | null | transformers | 26,160 | Entry not found |
KFlash/bert-finetuned-squad | 9f02cd755755ee3aaf8b14273ed862f0cda21ae7 | 2022-06-02T15:22:00.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | KFlash | null | KFlash/bert-finetuned-squad | 2 | null | transformers | 26,161 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- 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-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
jg/xlm-roberta-base-finetuned-panx-de | bccba5127ee0de2bf8398013594cf26a7327d4c3 | 2022-06-04T10:59:50.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | jg | null | jg/xlm-roberta-base-finetuned-panx-de | 2 | null | transformers | 26,162 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8620945214069894
---
<!-- 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
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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
GiordanoB/ptt5-base-portuguese-vocab-summarizacao-PTT-BR | bbeeacb3e4ae566bddafae79af1f09941b3fca5e | 2022-05-30T17:33:31.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | GiordanoB | null | GiordanoB/ptt5-base-portuguese-vocab-summarizacao-PTT-BR | 2 | null | transformers | 26,163 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ptt5-base-portuguese-vocab-summarizacao-PTT-BR
results: []
---
<!-- 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. -->
# ptt5-base-portuguese-vocab-summarizacao-PTT-BR
This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 15 | 4.6282 |
| No log | 2.0 | 30 | 3.9111 |
| No log | 3.0 | 45 | 3.6954 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
veronica320/EPC_ADEPT_roberta-l_all | fb4f2a0e9da0e2221464aef4d726d48fa03cc16b | 2022-05-30T01:20:15.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/EPC_ADEPT_roberta-l_all | 2 | null | transformers | 26,164 | Entry not found |
veronica320/SPTE_roberta-large-mnli_all | b2491a0ad751db3d08cc67ef11274c99391f381a | 2022-05-30T01:21:32.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/SPTE_roberta-large-mnli_all | 2 | null | transformers | 26,165 | Entry not found |
stevemobs/deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch | f665410350005f09073dc8b729d240221ee33def | 2022-05-30T09:12:36.000Z | [
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | stevemobs | null | stevemobs/deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch | 2 | null | transformers | 26,166 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch
results: []
---
<!-- 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. -->
# deberta-base-combined-squad1-aqa-1epoch-and-newsqa-1epoch
This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa-1epoch](https://huggingface.co/stevemobs/deberta-base-combined-squad1-aqa-1epoch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6807
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6654 | 1.0 | 17307 | 0.6807 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
xuio/roberta-sts12 | 1851713deafe7ad0c483852531ebacef685f3376 | 2022-05-30T04:08:14.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | xuio | null | xuio/roberta-sts12 | 2 | null | transformers | 26,167 | Entry not found |
hsuk/tiny-bert-sst2-distilled | 039fc84b6a245581a5064a6dbf77fe91355b9061 | 2022-06-05T07:24:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | hsuk | null | hsuk/tiny-bert-sst2-distilled | 2 | null | transformers | 26,168 | Entry not found |
bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-01 | cc81c6405acb5366d6170d4bf3efab01d2130e52 | 2022-06-16T13:36:05.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | bekirbakar | null | bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning-01 | 2 | null | transformers | 26,169 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-53-tr-fine-tuning-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-tr-fine-tuning-01
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
|
jkhan447/sarcasm-detection-Bert-base-uncased-CR | b2ec4d9ad85d85c5def7f822b26672f219bc677d | 2022-05-30T15:02:31.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | jkhan447 | null | jkhan447/sarcasm-detection-Bert-base-uncased-CR | 2 | null | transformers | 26,170 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sarcasm-detection-Bert-base-uncased-CR
results: []
---
<!-- 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. -->
# sarcasm-detection-Bert-base-uncased-CR
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: 3.2057
- Accuracy: 0.7187
## 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: 50
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
godwinh/distilbert-base-uncased-finetuned-clinc | 4c37c3ff13a19b7e896b625d55d3b8864f3359a6 | 2022-05-30T15:44:26.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | godwinh | null | godwinh/distilbert-base-uncased-finetuned-clinc | 2 | null | transformers | 26,171 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an clinc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7429 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8374 |
| 3.7868 | 3.0 | 954 | 1.1571 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8574 | 0.9132 |
| 0.9057 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
Splend1dchan/xtreme_s_xlsr_300m_mt5-small_minds14.en-US | df7dcdc0b1c71c634fdc9cbf7e2d1e5d209336a7 | 2022-05-30T12:33:15.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"en-US",
"dataset:xtreme_s",
"transformers",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_xlsr_300m_mt5-small_minds14.en-US | 2 | null | transformers | 26,172 | ---
language:
- en-US
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_xlsr_300m_mt5-small_minds14.en-US
results: []
---
<!-- 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. -->
# xtreme_s_xlsr_300m_mt5-small_minds14.en-US
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7321
- F1: 0.0154
- Accuracy: 0.0638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 2.6067 | 3.95 | 20 | 2.6501 | 0.0112 | 0.0851 |
| 2.5614 | 7.95 | 40 | 2.8018 | 0.0133 | 0.0603 |
| 2.2836 | 11.95 | 60 | 3.0786 | 0.0084 | 0.0603 |
| 1.9597 | 15.95 | 80 | 3.2288 | 0.0126 | 0.0638 |
| 1.5566 | 19.95 | 100 | 3.6934 | 0.0178 | 0.0567 |
| 1.3168 | 23.95 | 120 | 3.9135 | 0.0150 | 0.0638 |
| 1.0598 | 27.95 | 140 | 4.2618 | 0.0084 | 0.0603 |
| 0.5721 | 31.95 | 160 | 3.7973 | 0.0354 | 0.0780 |
| 0.4402 | 35.95 | 180 | 4.6233 | 0.0179 | 0.0638 |
| 0.6113 | 39.95 | 200 | 4.6149 | 0.0208 | 0.0674 |
| 0.3938 | 43.95 | 220 | 4.7886 | 0.0159 | 0.0638 |
| 0.2473 | 47.95 | 240 | 4.7321 | 0.0154 | 0.0638 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
zdreiosis/bert-finetuned-sem_eval-english | 900ee6cf8545751a3d1b53a8e30f48d3e9476be4 | 2022-05-31T02:36:16.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"3rd",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | zdreiosis | null | zdreiosis/bert-finetuned-sem_eval-english | 2 | null | transformers | 26,173 | ---
license: apache-2.0
tags:
- 3rd
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-sem_eval-english
results: []
---
<!-- 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-finetuned-sem_eval-english
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: 0.5536
- F1: 0.5455
- Roc Auc: 0.6968
- Accuracy: 0.1839
## 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: 30
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
g8a9/bert-base-italian-cased_ami20 | 69caa4ec07bea9e93aefbe91cc3e729d81f00d21 | 2022-05-30T12:55:07.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | g8a9 | null | g8a9/bert-base-italian-cased_ami20 | 2 | null | transformers | 26,174 | Entry not found |
Splend1dchan/xtreme_s_xlsr_300m_freeze_minds14.en-US | 353d03e0ee0f78bdc44af81beee0c60ca76bce1e | 2022-05-30T13:36:05.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_xlsr_300m_freeze_minds14.en-US | 2 | null | transformers | 26,175 | Entry not found |
Splend1dchan/xtreme_s_xlsr_300m_nofreeze_minds14.en-US | d890b6d1943ef8f4c55d361e751db5fe423ca6f4 | 2022-05-30T15:41:00.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"transformers"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_xlsr_300m_nofreeze_minds14.en-US | 2 | null | transformers | 26,176 | Entry not found |
joebobby/finetuning-sentiment-model-5000-samples3 | ce0e5d599610cfd75715979a4db99fd6c09adb2c | 2022-05-31T17:53:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | joebobby | null | joebobby/finetuning-sentiment-model-5000-samples3 | 2 | null | transformers | 26,177 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-5000-samples3
results: []
---
<!-- 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. -->
# finetuning-sentiment-model-5000-samples3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Jiexing/sparc_add_coref_t5_3b_order_0514_ckpt-5696 | 39b5026148ba2e7b38173612e3d41b25c760a363 | 2022-05-30T15:42:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jiexing | null | Jiexing/sparc_add_coref_t5_3b_order_0514_ckpt-5696 | 2 | null | transformers | 26,178 | Entry not found |
Mikey8943/marian-finetuned-kde4-en-to-fr | 8d2ccaa20dcf6cf1a9d621f6df21d4a67a6dd797 | 2022-05-30T17:16:08.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:kde4",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | Mikey8943 | null | Mikey8943/marian-finetuned-kde4-en-to-fr | 2 | null | transformers | 26,179 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 50.16950271131339
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Bleu: 50.1695
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
income/jpq-question_encoder-base-msmarco-distilbert-tas-b | d5e33cfc225d209ca94e395b0f22630d93542c17 | 2022-05-30T17:18:28.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-question_encoder-base-msmarco-distilbert-tas-b | 2 | null | transformers | 26,180 | ---
license: apache-2.0
---
|
income/jpq-document_encoder-base-msmarco-distilbert-tas-b | b963f87184025a9fa5217994f6fac65af2b108fe | 2022-05-30T17:23:56.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-document_encoder-base-msmarco-distilbert-tas-b | 2 | null | transformers | 26,181 | ---
license: apache-2.0
---
|
kimcando/reg_trained | 59ce88f0150635970aa450e2dead6bd5dc8dc13a | 2022-05-30T17:25:15.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | kimcando | null | kimcando/reg_trained | 2 | null | transformers | 26,182 | Entry not found |
ViktorDo/distilbert-base-uncased-scratch-powo_mgh_pt | b34f7f8e5aa284a53d750c02a75d3b4c250df71c | 2022-05-30T18:37:13.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | ViktorDo | null | ViktorDo/distilbert-base-uncased-scratch-powo_mgh_pt | 2 | null | transformers | 26,183 | ---
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-scratch-powo_mgh_pt
results: []
---
<!-- 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-scratch-powo_mgh_pt
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0408
## 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: 5
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 40
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 6.4584 | 0.2 | 200 | 4.7806 |
| 4.6385 | 0.41 | 400 | 4.3704 |
| 4.2219 | 0.61 | 600 | 4.0727 |
| 3.994 | 0.81 | 800 | 3.8772 |
| 3.8048 | 1.01 | 1000 | 3.6894 |
| 3.6722 | 1.22 | 1200 | 3.5732 |
| 3.4828 | 1.42 | 1400 | 3.4203 |
| 3.3648 | 1.62 | 1600 | 3.3634 |
| 3.3918 | 1.83 | 1800 | 3.2685 |
| 3.3919 | 2.03 | 2000 | 3.2027 |
| 3.1715 | 2.23 | 2200 | 3.1365 |
| 3.0635 | 2.43 | 2400 | 3.1228 |
| 3.0804 | 2.64 | 2600 | 3.0595 |
| 3.0468 | 2.84 | 2800 | 3.0318 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
erfangc/marian-finetuned-kde4-en-to-fr | 6a93d10ebfa40268802c7e949e7384ca9bf77da6 | 2022-05-31T01:44:37.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | erfangc | null | erfangc/marian-finetuned-kde4-en-to-fr | 2 | null | transformers | 26,184 | Entry not found |
Splend1dchan/xtreme_s_xlsr_300m_mt5-small_minds14.en-US_my | 83409e59f974454e996f9880e23ae00b30bcbbb3 | 2022-05-31T03:59:50.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"en-US",
"dataset:xtreme_s",
"transformers",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_xlsr_300m_mt5-small_minds14.en-US_my | 2 | null | transformers | 26,185 | ---
language:
- en-US
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_xlsr_300m_minds14.en-US_2
results: []
---
<!-- 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. -->
# xtreme_s_xlsr_300m_minds14.en-US_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)--concat-->mt5 on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5685
- F1: 0.83333
- Accuracy: 0.83258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
See TensorBoard
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Jiexing/cosql_add_coref_t5_3b_order_0519_ckpt-2624 | 27132d3dd34c2b86752cc54894abb8dd3419f787 | 2022-05-31T02:23:05.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jiexing | null | Jiexing/cosql_add_coref_t5_3b_order_0519_ckpt-2624 | 2 | null | transformers | 26,186 | Entry not found |
Splend1dchan/xtreme_s_w2v2_minds14.en-US | 0b28a0567cf95e4ef81ed4b16cdfcb7ff4a4178a | 2022-05-31T04:55:00.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"en-US",
"dataset:xtreme_s",
"transformers",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_w2v2_minds14.en-US | 2 | null | transformers | 26,187 | ---
language:
- en-US
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_w2v2_minds14.en-US
results: []
---
<!-- 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. -->
# xtreme_s_w2v2_minds14.en-US
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5337
- F1: 0.9144
- Accuracy: 0.9113
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|
| 2.6482 | 3.95 | 20 | 2.6421 | 0.0242 | 0.0745 |
| 2.6292 | 7.95 | 40 | 2.6359 | 0.0108 | 0.0816 |
| 2.5993 | 11.95 | 60 | 2.6301 | 0.0167 | 0.0674 |
| 2.4023 | 15.95 | 80 | 2.5514 | 0.1105 | 0.1454 |
| 1.4015 | 19.95 | 100 | 1.6843 | 0.5599 | 0.5851 |
| 0.4379 | 23.95 | 120 | 0.8126 | 0.7921 | 0.7908 |
| 0.0642 | 27.95 | 140 | 0.7178 | 0.8158 | 0.8156 |
| 0.0376 | 31.95 | 160 | 0.7286 | 0.8473 | 0.8475 |
| 0.0185 | 35.95 | 180 | 0.6779 | 0.8719 | 0.8723 |
| 0.0752 | 39.95 | 200 | 0.7096 | 0.8578 | 0.8511 |
| 0.0266 | 43.95 | 220 | 0.7655 | 0.8596 | 0.8546 |
| 0.0078 | 47.95 | 240 | 0.7623 | 0.8563 | 0.8511 |
| 0.007 | 51.95 | 260 | 0.6620 | 0.8794 | 0.8759 |
| 0.0047 | 55.95 | 280 | 0.5936 | 0.9045 | 0.9007 |
| 0.0067 | 59.95 | 300 | 0.8279 | 0.8546 | 0.8617 |
| 0.0394 | 63.95 | 320 | 0.8766 | 0.8359 | 0.8227 |
| 0.0051 | 67.95 | 340 | 0.8097 | 0.8483 | 0.8475 |
| 0.0095 | 71.95 | 360 | 0.6095 | 0.9083 | 0.9078 |
| 0.0026 | 75.95 | 380 | 0.5286 | 0.8889 | 0.8865 |
| 0.0023 | 79.95 | 400 | 0.7218 | 0.8926 | 0.8936 |
| 0.0023 | 83.95 | 420 | 0.6551 | 0.8997 | 0.8972 |
| 0.0027 | 87.95 | 440 | 0.6664 | 0.8848 | 0.8794 |
| 0.0019 | 91.95 | 460 | 0.5344 | 0.9032 | 0.9043 |
| 0.002 | 95.95 | 480 | 0.5863 | 0.8983 | 0.9007 |
| 0.0015 | 99.95 | 500 | 0.5715 | 0.9047 | 0.9043 |
| 0.0016 | 103.95 | 520 | 0.5615 | 0.8956 | 0.8936 |
| 0.0014 | 107.95 | 540 | 0.6353 | 0.8965 | 0.8936 |
| 0.0014 | 111.95 | 560 | 0.5593 | 0.9041 | 0.9007 |
| 0.0013 | 115.95 | 580 | 0.6041 | 0.8977 | 0.8936 |
| 0.0013 | 119.95 | 600 | 0.5794 | 0.9026 | 0.9007 |
| 0.0012 | 123.95 | 620 | 0.6858 | 0.9003 | 0.8972 |
| 0.0013 | 127.95 | 640 | 0.6730 | 0.9002 | 0.8972 |
| 0.0013 | 131.95 | 660 | 0.5707 | 0.9146 | 0.9113 |
| 0.0012 | 135.95 | 680 | 0.5604 | 0.9153 | 0.9113 |
| 0.0019 | 139.95 | 700 | 0.5468 | 0.9114 | 0.9078 |
| 0.0015 | 143.95 | 720 | 0.5361 | 0.9144 | 0.9113 |
| 0.0012 | 147.95 | 740 | 0.5337 | 0.9144 | 0.9113 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Splend1dchan/xtreme_s_w2v2_t5lephone-small_minds14.en-US | 51da1dd830dd45f5161947c66ebe07ec08ef1f77 | 2022-05-31T06:14:06.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"en-US",
"dataset:xtreme_s",
"transformers",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | null | false | Splend1dchan | null | Splend1dchan/xtreme_s_w2v2_t5lephone-small_minds14.en-US | 2 | null | transformers | 26,188 | ---
language:
- en-US
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_w2v2_t5lephone-small_minds14.en-US
results: []
---
<!-- 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. -->
# xtreme_s_w2v2_t5lephone-small_minds14.en-US
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the GOOGLE/XTREME_S - MINDS14.EN-US dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5203
- F1: 0.7526
- Accuracy: 0.7518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|
| 2.589 | 3.95 | 20 | 2.6401 | 0.0108 | 0.0816 |
| 2.5223 | 7.95 | 40 | 2.6493 | 0.0339 | 0.0816 |
| 2.5085 | 11.95 | 60 | 2.6236 | 0.0539 | 0.1028 |
| 2.1252 | 15.95 | 80 | 2.5006 | 0.1458 | 0.1667 |
| 1.3711 | 19.95 | 100 | 2.2712 | 0.2344 | 0.2837 |
| 1.5092 | 23.95 | 120 | 2.0599 | 0.3631 | 0.3936 |
| 0.4962 | 27.95 | 140 | 1.8475 | 0.4881 | 0.4894 |
| 0.4169 | 31.95 | 160 | 1.8262 | 0.5358 | 0.5142 |
| 0.1579 | 35.95 | 180 | 1.6481 | 0.5967 | 0.6028 |
| 0.0927 | 39.95 | 200 | 1.4470 | 0.6748 | 0.6560 |
| 0.1363 | 43.95 | 220 | 1.2725 | 0.6836 | 0.6879 |
| 0.1324 | 47.95 | 240 | 1.4330 | 0.6653 | 0.6702 |
| 0.0294 | 51.95 | 260 | 1.2978 | 0.7079 | 0.7163 |
| 0.0326 | 55.95 | 280 | 1.3869 | 0.6823 | 0.6879 |
| 0.0444 | 59.95 | 300 | 1.5764 | 0.7051 | 0.6986 |
| 0.0527 | 63.95 | 320 | 2.2013 | 0.5899 | 0.5851 |
| 0.1542 | 67.95 | 340 | 1.5203 | 0.7053 | 0.6986 |
| 0.0127 | 71.95 | 360 | 1.7149 | 0.7105 | 0.7128 |
| 0.0105 | 75.95 | 380 | 1.2471 | 0.7853 | 0.7837 |
| 0.009 | 79.95 | 400 | 1.5720 | 0.7065 | 0.7057 |
| 0.0081 | 83.95 | 420 | 1.9395 | 0.6656 | 0.6702 |
| 0.2345 | 87.95 | 440 | 1.5704 | 0.7408 | 0.7411 |
| 0.0076 | 91.95 | 460 | 1.4706 | 0.7554 | 0.7589 |
| 0.0064 | 95.95 | 480 | 1.5746 | 0.7491 | 0.7518 |
| 0.3105 | 99.95 | 500 | 1.6824 | 0.7273 | 0.7376 |
| 0.0058 | 103.95 | 520 | 1.3799 | 0.7474 | 0.7624 |
| 0.0055 | 107.95 | 540 | 1.4086 | 0.7350 | 0.7518 |
| 0.0051 | 111.95 | 560 | 1.2832 | 0.7874 | 0.7979 |
| 0.0052 | 115.95 | 580 | 1.3474 | 0.7752 | 0.7801 |
| 0.0046 | 119.95 | 600 | 1.6125 | 0.7451 | 0.7482 |
| 0.0044 | 123.95 | 620 | 1.5927 | 0.7486 | 0.7518 |
| 0.0044 | 127.95 | 640 | 1.5551 | 0.7487 | 0.7518 |
| 0.0041 | 131.95 | 660 | 1.5117 | 0.7631 | 0.7660 |
| 0.0041 | 135.95 | 680 | 1.5210 | 0.7577 | 0.7624 |
| 0.0041 | 139.95 | 700 | 1.5145 | 0.7655 | 0.7660 |
| 0.004 | 143.95 | 720 | 1.5053 | 0.7665 | 0.7660 |
| 0.004 | 147.95 | 740 | 1.5203 | 0.7526 | 0.7518 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
bdh240901/wav2vec2-large-xls-r-300m-vi-colab | 68d771f4c3ef3bec8c355b729b890e7eba7fb571 | 2022-05-31T06:11:31.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | bdh240901 | null | bdh240901/wav2vec2-large-xls-r-300m-vi-colab | 2 | null | transformers | 26,189 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-vi-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-vi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
PontifexMaximus/opus-mt-iir-en-finetuned-fa-to-en | 4a607104849f79b6317bab04d4dfc6674eb9c405 | 2022-06-02T09:38:06.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:opus_infopankki",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | PontifexMaximus | null | PontifexMaximus/opus-mt-iir-en-finetuned-fa-to-en | 2 | null | transformers | 26,190 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_infopankki
metrics:
- bleu
model-index:
- name: opus-mt-iir-en-finetuned-fa-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_infopankki
type: opus_infopankki
args: en-fa
metrics:
- name: Bleu
type: bleu
value: 36.687
---
<!-- 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. -->
# opus-mt-iir-en-finetuned-fa-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-iir-en](https://huggingface.co/Helsinki-NLP/opus-mt-iir-en) on the opus_infopankki dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0968
- Bleu: 36.687
- Gen Len: 16.039
## 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-06
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 3.1614 | 1.0 | 1509 | 2.8058 | 12.326 | 16.5467 |
| 2.7235 | 2.0 | 3018 | 2.4178 | 15.6912 | 16.6396 |
| 2.4839 | 3.0 | 4527 | 2.1905 | 18.1971 | 16.4884 |
| 2.3044 | 4.0 | 6036 | 2.0272 | 20.197 | 16.4735 |
| 2.1943 | 5.0 | 7545 | 1.9012 | 22.2265 | 16.4266 |
| 2.0669 | 6.0 | 9054 | 1.7984 | 23.7711 | 16.353 |
| 1.985 | 7.0 | 10563 | 1.7100 | 24.986 | 16.284 |
| 1.9024 | 8.0 | 12072 | 1.6346 | 26.1758 | 16.217 |
| 1.8484 | 9.0 | 13581 | 1.5692 | 27.2782 | 16.1924 |
| 1.7761 | 10.0 | 15090 | 1.5111 | 28.2761 | 16.144 |
| 1.733 | 11.0 | 16599 | 1.4599 | 29.2184 | 16.2438 |
| 1.6772 | 12.0 | 18108 | 1.4150 | 30.0026 | 16.1949 |
| 1.6297 | 13.0 | 19617 | 1.3743 | 30.7839 | 16.1565 |
| 1.5918 | 14.0 | 21126 | 1.3370 | 31.4921 | 16.1323 |
| 1.5548 | 15.0 | 22635 | 1.3038 | 32.0621 | 16.076 |
| 1.5333 | 16.0 | 24144 | 1.2743 | 32.6881 | 16.0078 |
| 1.5145 | 17.0 | 25653 | 1.2478 | 33.3794 | 16.1228 |
| 1.4826 | 18.0 | 27162 | 1.2240 | 33.8335 | 16.0809 |
| 1.4488 | 19.0 | 28671 | 1.2021 | 34.2819 | 16.0479 |
| 1.4386 | 20.0 | 30180 | 1.1829 | 34.7206 | 16.0578 |
| 1.4127 | 21.0 | 31689 | 1.1660 | 35.031 | 16.0717 |
| 1.4089 | 22.0 | 33198 | 1.1510 | 35.4142 | 16.0391 |
| 1.3922 | 23.0 | 34707 | 1.1380 | 35.6777 | 16.0461 |
| 1.377 | 24.0 | 36216 | 1.1273 | 35.95 | 16.0569 |
| 1.3598 | 25.0 | 37725 | 1.1175 | 36.2435 | 16.0426 |
| 1.3515 | 26.0 | 39234 | 1.1097 | 36.4009 | 16.0247 |
| 1.3441 | 27.0 | 40743 | 1.1042 | 36.4815 | 16.0447 |
| 1.3412 | 28.0 | 42252 | 1.1001 | 36.6092 | 16.0489 |
| 1.3527 | 29.0 | 43761 | 1.0976 | 36.6703 | 16.0383 |
| 1.3397 | 30.0 | 45270 | 1.0968 | 36.687 | 16.039 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.7.1+cu110
- Datasets 2.2.2
- Tokenizers 0.12.1
|
theojolliffe/bart-cnn-science-v3-e3 | 5a20f2a8ea7616df4386cdd7aa792b65f29d6a0d | 2022-05-31T08:34:03.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-cnn-science-v3-e3 | 2 | null | transformers | 26,191 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-science-v3-e3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-science-v3-e3
This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8586
- Rouge1: 53.3497
- Rouge2: 34.0001
- Rougel: 35.6149
- Rougelsum: 50.5723
- Gen Len: 141.3519
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 0.9977 | 51.8104 | 31.5395 | 33.6887 | 49.2385 | 142.0 |
| 1.1785 | 2.0 | 796 | 0.8875 | 53.7817 | 34.5394 | 35.9556 | 51.3317 | 141.537 |
| 0.7376 | 3.0 | 1194 | 0.8586 | 53.3497 | 34.0001 | 35.6149 | 50.5723 | 141.3519 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jkhan447/sarcasm-detection-xlnet-base-cased | 8f73606f35d21007b31883b684d6157b909fc48e | 2022-05-31T14:17:58.000Z | [
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | jkhan447 | null | jkhan447/sarcasm-detection-xlnet-base-cased | 2 | null | transformers | 26,192 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sarcasm-detection-xlnet-base-cased
results: []
---
<!-- 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. -->
# sarcasm-detection-xlnet-base-cased
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1470
- Accuracy: 0.7117
## 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: 50
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Miss/vit-base-beans-demo-v5 | 6970d60f4438345ef3514a264b6ade0abad95073 | 2022-06-30T01:19:04.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers"
] | image-classification | false | Miss | null | Miss/vit-base-beans-demo-v5 | 2 | null | transformers | 26,193 | Entry not found |
eetnawa/StereoKG-DT-SK | 420761ed630b209dcae9ec61bb1cdd594bda1758 | 2022-05-31T10:35:19.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | eetnawa | null | eetnawa/StereoKG-DT-SK | 2 | null | transformers | 26,194 | ---
license: mit
---
|
eetnawa/StereoKG-DT-UK | 4fdf0f19f1ed2b88c9f3214c64a90b4a1518f5a9 | 2022-05-31T13:11:14.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | eetnawa | null | eetnawa/StereoKG-DT-UK | 2 | null | transformers | 26,195 | ---
license: mit
---
|
PSW/samsum_reverse_train_min300_max1000_epoch6 | 5c0b71604f490744478e2fb0d871609bcf5b4b2e | 2022-05-31T15:19:24.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/samsum_reverse_train_min300_max1000_epoch6 | 2 | null | transformers | 26,196 | Entry not found |
joaogante/test_img | 99ff23f1c79ca06a9a95e75e7bd19b9531c2c20e | 2022-05-31T15:44:12.000Z | [
"pytorch",
"jax",
"vit",
"feature-extraction",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | feature-extraction | false | joaogante | null | joaogante/test_img | 2 | null | transformers | 26,197 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
inference: false
---
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
Here is how to use this model in JAX/Flax:
```python
from transformers import ViTFeatureExtractor, FlaxViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = FlaxViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
inputs = feature_extractor(images=image, return_tensors="np")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
``` |
irenelizihui/bert-finetuned-squad | 9c91eb47d632756dfca0360a7fda187ba624dd75 | 2022-06-01T02:53:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | irenelizihui | null | irenelizihui/bert-finetuned-squad | 2 | null | transformers | 26,198 | Entry not found |
muhtasham/RoBERTa-tg | 1bbba8c0f258fb487ca6ce285f139ad1bb900c6e | 2022-06-01T07:52:30.000Z | [
"pytorch",
"roberta",
"fill-mask",
"tg",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | muhtasham | null | muhtasham/RoBERTa-tg | 2 | 1 | transformers | 26,199 | ---
language:
- tg
widget:
- text: "Пойтахти <mask> Душанбе"
- text: "<mask> ба ин сайти шумо медароям."
- text: "Номи ман Акрам <mask>"
tags:
- generated_from_trainer
model-index:
- name: RoBERTa-tg
results: []
---
<!-- 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. -->
# RoBERTa-tg
This model is a fine-tuned version of [Tajik-Corpus](https://huggingface.co/datasets/muhtasham/tajik-corpus) dataset which is based on Leipzig Corpora.
## Model description
You can use model for masked text generation or fine-tune it to a downstream task.
## 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: 128
- eval_batch_size: 8
- seed: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
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