sha null | last_modified null | library_name stringclasses 154 values | text stringlengths 1 900k | metadata stringlengths 2 348k | pipeline_tag stringclasses 45 values | id stringlengths 5 122 | tags listlengths 1 1.84k | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 | tokens_length listlengths 1 723 | input_texts listlengths 1 61 | embeddings listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | transformers | **Usage HuggingFace Transformers for question generation task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-QuestionGeneration")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
input_ = tokenizer.batch_encode_plus([input_text], max_length=1024, pad_to_max_length=True,
truncation=True, padding='longest', return_tensors='pt')
input_ids = input_['input_ids']
input_mask = input_['attention_mask']
questions = model.generate(input_ids=input_ids,
attention_mask=input_mask,
num_beams=32,
no_repeat_ngram_size=2,
early_stopping=True,
num_return_sequences=10)
questions = tokenizer.batch_decode(questions, skip_special_tokens=True)
```
**Decoder configuration examples:**
[**Input text you can see here**](https://www.bbc.com/news/science-environment-59775105)
```
questions = model.generate(input_ids=input_ids,
attention_mask=input_mask,
num_beams=32,
no_repeat_ngram_size=2,
early_stopping=True,
num_return_sequences=10)
tokenizer.batch_decode(questions, skip_special_tokens=True)
```
output:
1. *What is the impact of human induced climate change on tropical cyclones?*
2. *What is the impact of climate change on tropical cyclones?*
3. *What is the impact of human induced climate change on tropical cyclone formation?*
4. *How many tropical cyclones will occur in the mid-latitudes?*
5. *What is the impact of climate change on the formation of tropical cyclones?*
6. *Is it possible for a tropical cyclone to form in the middle latitudes?*
7. *How many tropical cyclones will be formed in the mid-latitudes?*
8. *How many tropical cyclones will there be in the mid-latitudes?*
9. *How many tropical cyclones will form in the mid-latitudes?*
10. *What is the impact of global warming on tropical cyclones?*
11. *How long does it take for a tropical cyclone to form?*
12. 'What are the impacts of climate change on tropical cyclones?*
13. *What are the effects of climate change on tropical cyclones?*
14. *How many tropical cyclones will be able to form in the middle latitudes?*
15. *What is the impact of climate change on tropical cyclone formation?*
16. *What is the effect of climate change on tropical cyclones?*
17. *How long does it take for a tropical cyclone to form in the middle latitude?*
18. *How many tropical cyclones will occur in the middle latitudes?*
19. *How many tropical cyclones are likely to form in the midlatitudes?*
20. *How many tropical cyclones are likely to form in the middle latitudes?*
21. *How many tropical cyclones are expected to form in the midlatitudes?*
22. *How many tropical cyclones will be formed in the middle latitudes?*
23. *How many tropical cyclones will there be in the middle latitudes?*
24. *How long will it take for a tropical cyclone to form in the middle latitude?*
25. *What is the impact of global warming on tropical cyclone formation?*
26. *How many tropical cyclones will form in the middle latitudes?*
27. *How many tropical cyclones can we expect to form in the middle latitudes?*
28. *Is it possible for a tropical cyclone to form in the middle latitude?*
29. *What is the effect of climate change on tropical cyclone formation?*
30. *What are the effects of climate change on tropical cyclone formation?*
Also you can play with the following parameters in generate method:
-top_k
-top_p
[**Meaning of parameters to generate text you can see here**](https://huggingface.co/blog/how-to-generate) | {} | text2text-generation | AlekseyKulnevich/Pegasus-QuestionGeneration | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Usage HuggingFace Transformers for question generation task
Decoder configuration examples:
Input text you can see here
output:
1. *What is the impact of human induced climate change on tropical cyclones?*
2. *What is the impact of climate change on tropical cyclones?*
3. *What is the impact of human induced climate change on tropical cyclone formation?*
4. *How many tropical cyclones will occur in the mid-latitudes?*
5. *What is the impact of climate change on the formation of tropical cyclones?*
6. *Is it possible for a tropical cyclone to form in the middle latitudes?*
7. *How many tropical cyclones will be formed in the mid-latitudes?*
8. *How many tropical cyclones will there be in the mid-latitudes?*
9. *How many tropical cyclones will form in the mid-latitudes?*
10. *What is the impact of global warming on tropical cyclones?*
11. *How long does it take for a tropical cyclone to form?*
12. 'What are the impacts of climate change on tropical cyclones?*
13. *What are the effects of climate change on tropical cyclones?*
14. *How many tropical cyclones will be able to form in the middle latitudes?*
15. *What is the impact of climate change on tropical cyclone formation?*
16. *What is the effect of climate change on tropical cyclones?*
17. *How long does it take for a tropical cyclone to form in the middle latitude?*
18. *How many tropical cyclones will occur in the middle latitudes?*
19. *How many tropical cyclones are likely to form in the midlatitudes?*
20. *How many tropical cyclones are likely to form in the middle latitudes?*
21. *How many tropical cyclones are expected to form in the midlatitudes?*
22. *How many tropical cyclones will be formed in the middle latitudes?*
23. *How many tropical cyclones will there be in the middle latitudes?*
24. *How long will it take for a tropical cyclone to form in the middle latitude?*
25. *What is the impact of global warming on tropical cyclone formation?*
26. *How many tropical cyclones will form in the middle latitudes?*
27. *How many tropical cyclones can we expect to form in the middle latitudes?*
28. *Is it possible for a tropical cyclone to form in the middle latitude?*
29. *What is the effect of climate change on tropical cyclone formation?*
30. *What are the effects of climate change on tropical cyclone formation?*
Also you can play with the following parameters in generate method:
-top_k
-top_p
Meaning of parameters to generate text you can see here | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.027585696429014206,
0.007313489448279142,
-0.007987958379089832,
0.028276974335312843,
0.1623227447271347,
0.029489101842045784,
0.14388062059879303,
0.1242440864443779,
0.009850045666098595,
-0.03792005777359009,
0.13300150632858276,
0.18938398361206055,
-0.010199892334640026,
0.117705... |
null | null | transformers | **Usage HuggingFace Transformers for summarization task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-Summarization")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
input_ = tokenizer.batch_encode_plus([input_text], max_length=1024, pad_to_max_length=True,
truncation=True, padding='longest', return_tensors='pt')
input_ids = input_['input_ids']
input_mask = input_['attention_mask']
summary = model.generate(input_ids=input_ids,
attention_mask=input_mask,
num_beams=32,
min_length=100,
no_repeat_ngram_size=2,
early_stopping=True,
num_return_sequences=10)
questions = tokenizer.batch_decode(summary, skip_special_tokens=True)
```
**Decoder configuration examples:**
[**Input text you can see here**](https://www.bbc.com/news/science-environment-59775105)
```
summary = model.generate(input_ids=input_ids,
attention_mask=input_mask,
num_beams=32,
min_length=100,
no_repeat_ngram_size=2,
early_stopping=True,
num_return_sequences=1)
tokenizer.batch_decode(summary, skip_special_tokens=True)
```
output:
1. *global warming will expand the range of tropical cyclones in the mid-latitudes of the world, according to a new study published by the Intergovernmental Panel on Climate change (IPCC) and the US National Oceanic and Atmospheric Administration (NOAA) The study shows that a warming climate will allow more of these types of storms to form over a wider range than they have been able to do over the past three million years. "As the climate warms, it's likely that these storms will become more frequent and more intense," said the authors of this study.*
```
summary = model.generate(input_ids=input_ids,
attention_mask=input_mask,
top_k=30,
no_repeat_ngram_size=2,
early_stopping=True,
min_length=100,
num_return_sequences=1)
tokenizer.batch_decode(summary, skip_special_tokens=True)
```
output:
1. *tropical cyclones in the mid-latitudes of the world will likely form more of these types of storms, according to a new study published by the Intergovernmental Panel on Climate change (IPCC) on the impact of human induced climate change on these storms. The study shows that a warming climate will increase the likelihood of a subtropical cyclone forming over a wider range of latitudes, including the equator, than it has been for the past three million years, and that it will be more likely to form over the tropics.*
Also you can play with the following parameters in generate method:
-top_k
-top_p
[**Meaning of parameters to generate text you can see here**](https://huggingface.co/blog/how-to-generate)
| {} | text2text-generation | AlekseyKulnevich/Pegasus-Summarization | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Usage HuggingFace Transformers for summarization task
Decoder configuration examples:
Input text you can see here
output:
1. *global warming will expand the range of tropical cyclones in the mid-latitudes of the world, according to a new study published by the Intergovernmental Panel on Climate change (IPCC) and the US National Oceanic and Atmospheric Administration (NOAA) The study shows that a warming climate will allow more of these types of storms to form over a wider range than they have been able to do over the past three million years. "As the climate warms, it's likely that these storms will become more frequent and more intense," said the authors of this study.*
output:
1. *tropical cyclones in the mid-latitudes of the world will likely form more of these types of storms, according to a new study published by the Intergovernmental Panel on Climate change (IPCC) on the impact of human induced climate change on these storms. The study shows that a warming climate will increase the likelihood of a subtropical cyclone forming over a wider range of latitudes, including the equator, than it has been for the past three million years, and that it will be more likely to form over the tropics.*
Also you can play with the following parameters in generate method:
-top_k
-top_p
Meaning of parameters to generate text you can see here
| [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.027585696429014206,
0.007313489448279142,
-0.007987958379089832,
0.028276974335312843,
0.1623227447271347,
0.029489101842045784,
0.14388062059879303,
0.1242440864443779,
0.009850045666098595,
-0.03792005777359009,
0.13300150632858276,
0.18938398361206055,
-0.010199892334640026,
0.117705... |
null | null | transformers |
This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly!
| {"language": "en", "tags": ["text-generation"], "pipeline_tag": "text-generation", "widget": [{"text": "The Gods"}, {"text": "What is"}]} | text-generation | Alerosae/SocratesGPT-2 | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"en",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #feature-extraction #text-generation #en #endpoints_compatible #text-generation-inference #region-us
|
This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly!
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #feature-extraction #text-generation #en #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #feature-extraction #text-generation #en #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.0282889436930418,
0.0009478123974986374,
-0.007868082262575626,
0.01403009332716465,
0.1635991632938385,
0.04000629112124443,
0.02275751158595085,
0.14084503054618835,
-0.017932232469320297,
-0.003716195933520794,
0.13020537793636322,
0.20026414096355438,
0.011470472440123558,
0.0121601... |
null | null | transformers | # XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
## Used QA Datasets
SQuAD + SberQuAD
[SberQuAD original paper](https://arxiv.org/pdf/1912.09723.pdf) is here! Recommend to read!
## Evaluation results
The results obtained are the following (SberQUaD):
```
f1 = 84.3
exact_match = 65.3
| {"language": ["en", "ru", "multilingual"], "license": "apache-2.0"} | question-answering | AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ru",
"multilingual",
"arxiv:1912.09723",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1912.09723"
] | [
"en",
"ru",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| # XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
## Used QA Datasets
SQuAD + SberQuAD
SberQuAD original paper is here! Recommend to read!
## Evaluation results
The results obtained are the following (SberQUaD):
'''
f1 = 84.3
exact_match = 65.3
| [
"# XLM-RoBERTa large model whole word masking finetuned on SQuAD\nPretrained model using a masked language modeling (MLM) objective. \nFine tuned on English and Russian QA datasets",
"## Used QA Datasets\nSQuAD + SberQuAD\n\nSberQuAD original paper is here! Recommend to read!",
"## Evaluation results\nThe resul... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# XLM-RoBERTa large model whole word masking finetuned on SQuAD\nPretrained model using a masked language modeling (MLM) objective. \nFine tu... | [
62,
49,
31,
32
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #question-answering #en #ru #multilingual #arxiv-1912.09723 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# XLM-RoBERTa large model whole word masking finetuned on SQuAD\nPretrained model using a masked language modeling (MLM) objective. \nFine... | [
-0.11333110928535461,
-0.008323971182107925,
-0.0032044195104390383,
0.05671990290284157,
0.12199054658412933,
-0.025212423875927925,
0.07439691573381424,
0.053967222571372986,
0.011802053079009056,
0.025689464062452316,
0.1041514053940773,
-0.002655280288308859,
-0.0005556242540478706,
0.... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentence-compression-roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3465
- Accuracy: 0.8473
- F1: 0.6835
- Precision: 0.6835
- Recall: 0.6835
## 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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5312 | 1.0 | 50 | 0.5251 | 0.7591 | 0.0040 | 0.75 | 0.0020 |
| 0.4 | 2.0 | 100 | 0.4003 | 0.8200 | 0.5341 | 0.7113 | 0.4275 |
| 0.3355 | 3.0 | 150 | 0.3465 | 0.8473 | 0.6835 | 0.6835 | 0.6835 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentence-compression-roberta", "results": []}]} | token-classification | AlexMaclean/sentence-compression-roberta | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sentence-compression-roberta
============================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3465
* Accuracy: 0.8473
* F1: 0.6835
* Precision: 0.6835
* Recall: 0.6835
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: 16
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eva... | [
50,
116,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* ... | [
-0.08405987173318863,
0.07927323877811432,
-0.0014021005481481552,
0.12081235647201538,
0.18684478104114532,
0.025732723996043205,
0.11298175156116486,
0.12693557143211365,
-0.09936793148517609,
0.018990756943821907,
0.13045480847358704,
0.17961297929286957,
0.003609556006267667,
0.1471073... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentence-compression
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Accuracy: 0.8912
- F1: 0.8367
- Precision: 0.8495
- Recall: 0.8243
## 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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2686 | 1.0 | 10000 | 0.2667 | 0.8894 | 0.8283 | 0.8725 | 0.7884 |
| 0.2205 | 2.0 | 20000 | 0.2704 | 0.8925 | 0.8372 | 0.8579 | 0.8175 |
| 0.1476 | 3.0 | 30000 | 0.2973 | 0.8912 | 0.8367 | 0.8495 | 0.8243 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentence-compression", "results": []}]} | token-classification | AlexMaclean/sentence-compression | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sentence-compression
====================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2973
* Accuracy: 0.8912
* F1: 0.8367
* Precision: 0.8495
* Recall: 0.8243
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: 16
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2388
- Wer: 0.3681
## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3748 | 0.07 | 500 | 3.8784 | 1.0 |
| 2.8068 | 0.14 | 1000 | 2.8289 | 0.9826 |
| 1.6698 | 0.22 | 1500 | 0.8811 | 0.7127 |
| 1.3488 | 0.29 | 2000 | 0.5166 | 0.5369 |
| 1.2239 | 0.36 | 2500 | 0.4105 | 0.4741 |
| 1.1537 | 0.43 | 3000 | 0.3585 | 0.4448 |
| 1.1184 | 0.51 | 3500 | 0.3336 | 0.4292 |
| 1.0968 | 0.58 | 4000 | 0.3195 | 0.4180 |
| 1.0737 | 0.65 | 4500 | 0.3075 | 0.4141 |
| 1.0677 | 0.72 | 5000 | 0.3015 | 0.4089 |
| 1.0462 | 0.8 | 5500 | 0.2971 | 0.4077 |
| 1.0392 | 0.87 | 6000 | 0.2870 | 0.3997 |
| 1.0178 | 0.94 | 6500 | 0.2805 | 0.3963 |
| 0.992 | 1.01 | 7000 | 0.2748 | 0.3935 |
| 1.0197 | 1.09 | 7500 | 0.2691 | 0.3884 |
| 1.0056 | 1.16 | 8000 | 0.2682 | 0.3889 |
| 0.9826 | 1.23 | 8500 | 0.2647 | 0.3868 |
| 0.9815 | 1.3 | 9000 | 0.2603 | 0.3832 |
| 0.9717 | 1.37 | 9500 | 0.2561 | 0.3807 |
| 0.9605 | 1.45 | 10000 | 0.2523 | 0.3783 |
| 0.96 | 1.52 | 10500 | 0.2494 | 0.3788 |
| 0.9442 | 1.59 | 11000 | 0.2478 | 0.3760 |
| 0.9564 | 1.66 | 11500 | 0.2454 | 0.3733 |
| 0.9436 | 1.74 | 12000 | 0.2439 | 0.3747 |
| 0.938 | 1.81 | 12500 | 0.2411 | 0.3716 |
| 0.9353 | 1.88 | 13000 | 0.2397 | 0.3698 |
| 0.9271 | 1.95 | 13500 | 0.2388 | 0.3681 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-300m-fr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 8.0 fr", "type": "mozilla-foundation/common_voice_8_0", "args": "fr"}, "metrics": [{"type": "wer", "value": 36.81, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "fr"}, "metrics": [{"type": "wer", "value": 35.55, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "fr"}, "metrics": [{"type": "wer", "value": 39.94, "name": "Test WER"}]}]}]} | automatic-speech-recognition | AlexN/xls-r-300m-fr-0 | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #fr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - FR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2388
* Wer: 0.3681
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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 2.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
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0.11087358742952347... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2700
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"language": ["fr"], "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-300m-fr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 8.0 fr", "type": "mozilla-foundation/common_voice_8_0", "args": "fr"}, "metrics": [{"type": "wer", "value": 21.58, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "fr"}, "metrics": [{"type": "wer", "value": 36.03, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "fr"}, "metrics": [{"type": "wer", "value": 38.86, "name": "Test WER"}]}]}]} | automatic-speech-recognition | AlexN/xls-r-300m-fr | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #fr #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2700
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2290
- Wer: 0.2382
## 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: 0.0002
- 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
- lr_scheduler_warmup_steps: 1500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0952 | 0.64 | 500 | 3.0982 | 1.0 |
| 1.7975 | 1.29 | 1000 | 0.7887 | 0.5651 |
| 1.4138 | 1.93 | 1500 | 0.5238 | 0.4389 |
| 1.344 | 2.57 | 2000 | 0.4775 | 0.4318 |
| 1.2737 | 3.21 | 2500 | 0.4648 | 0.4075 |
| 1.2554 | 3.86 | 3000 | 0.4069 | 0.3678 |
| 1.1996 | 4.5 | 3500 | 0.3914 | 0.3668 |
| 1.1427 | 5.14 | 4000 | 0.3694 | 0.3572 |
| 1.1372 | 5.78 | 4500 | 0.3568 | 0.3501 |
| 1.0831 | 6.43 | 5000 | 0.3331 | 0.3253 |
| 1.1074 | 7.07 | 5500 | 0.3332 | 0.3352 |
| 1.0536 | 7.71 | 6000 | 0.3131 | 0.3152 |
| 1.0248 | 8.35 | 6500 | 0.3024 | 0.3023 |
| 1.0075 | 9.0 | 7000 | 0.2948 | 0.3028 |
| 0.979 | 9.64 | 7500 | 0.2796 | 0.2853 |
| 0.9594 | 10.28 | 8000 | 0.2719 | 0.2789 |
| 0.9172 | 10.93 | 8500 | 0.2620 | 0.2695 |
| 0.9047 | 11.57 | 9000 | 0.2537 | 0.2596 |
| 0.8777 | 12.21 | 9500 | 0.2438 | 0.2525 |
| 0.8629 | 12.85 | 10000 | 0.2409 | 0.2493 |
| 0.8575 | 13.5 | 10500 | 0.2366 | 0.2440 |
| 0.8361 | 14.14 | 11000 | 0.2317 | 0.2385 |
| 0.8126 | 14.78 | 11500 | 0.2290 | 0.2382 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-300m-pt", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 8.0 pt", "type": "mozilla-foundation/common_voice_8_0", "args": "pt"}, "metrics": [{"type": "wer", "value": 19.361, "name": "Test WER"}, {"type": "cer", "value": 5.533, "name": "Test CER"}, {"type": "wer", "value": 19.36, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "fr"}, "metrics": [{"type": "wer", "value": 47.812, "name": "Validation WER"}, {"type": "cer", "value": 18.805, "name": "Validation CER"}, {"type": "wer", "value": 48.01, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "pt"}, "metrics": [{"type": "wer", "value": 49.21, "name": "Test WER"}]}]}]} | automatic-speech-recognition | AlexN/xls-r-300m-pt | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hf-asr-leaderboard #pt #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2290
* Wer: 0.2382
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: 0.0002
* 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
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
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0.12306997925... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cola
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7552
- Matthews Correlation: 0.5495
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "args": "cola"}, "metric": {"name": "Matthews Correlation", "type": "matthews_correlation", "value": 0.5494768667363472}}]}]} | text-classification | Alireza1044/albert-base-v2-cola | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# cola
This model is a fine-tuned version of albert-base-v2 on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7552
- Matthews Correlation: 0.5495
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
"# cola\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE COLA dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7552\n- Matthews Correlation: 0.5495",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
... | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.8501
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "mnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.8500813669650122}}]}]} | text-classification | Alireza1044/albert-base-v2-mnli | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mnli
This model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Accuracy: 0.8501
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
"# mnli\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE MNLI dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5383\n- Accuracy: 0.8501",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mrpc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4171
- Accuracy: 0.8627
- F1: 0.9011
- Combined Score: 0.8819
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "mrpc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metric": {"name": "F1", "type": "f1", "value": 0.901060070671378}}]}]} | text-classification | Alireza1044/albert-base-v2-mrpc | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mrpc
This model is a fine-tuned version of albert-base-v2 on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4171
- Accuracy: 0.8627
- F1: 0.9011
- Combined Score: 0.8819
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
"# mrpc\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4171\n- Accuracy: 0.8627\n- F1: 0.9011\n- Combined Score: 0.8819",
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0.1542724519... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Accuracy: 0.9138
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "qnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9137836353651839}}]}]} | text-classification | Alireza1044/albert-base-v2-qnli | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# qnli
This model is a fine-tuned version of albert-base-v2 on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Accuracy: 0.9138
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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0.12361826... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qqp
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886
## 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "qqp", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE QQP", "type": "glue", "args": "qqp"}, "metric": {"name": "F1", "type": "f1", "value": 0.8722569490623753}}]}]} | text-classification | Alireza1044/albert-base-v2-qqp | [
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# qqp
This model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3695
- Accuracy: 0.9050
- F1: 0.8723
- Combined Score: 0.8886
## 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
"# qqp\n\nThis model is a fine-tuned version of albert-base-v2 on the GLUE QQP dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3695\n- Accuracy: 0.9050\n- F1: 0.8723\n- Combined Score: 0.8886",
"## Model description\n\nMore information needed",
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0.148151054978... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rte
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7994
- Accuracy: 0.6859
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "rte", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.6859205776173285}}]}]} | text-classification | Alireza1044/albert-base-v2-rte | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# rte
This model is a fine-tuned version of albert-base-v2 on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7994
- Accuracy: 0.6859
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sst2
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3808
- Accuracy: 0.9232
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9231651376146789}}]}]} | text-classification | Alireza1044/albert-base-v2-sst2 | [
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"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# sst2
This model is a fine-tuned version of albert-base-v2 on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3808
- Accuracy: 0.9232
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stsb
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3978
- Pearson: 0.9090
- Spearmanr: 0.9051
- Combined Score: 0.9071
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model_index": [{"name": "stsb", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "args": "stsb"}, "metric": {"name": "Spearmanr", "type": "spearmanr", "value": 0.9050744778895732}}]}]} | text-classification | Alireza1044/albert-base-v2-stsb | [
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"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# stsb
This model is a fine-tuned version of albert-base-v2 on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3978
- Pearson: 0.9090
- Spearmanr: 0.9051
- Combined Score: 0.9071
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898
- Accuracy: 0.5634
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "wnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "args": "wnli"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.5633802816901409}}]}]} | text-classification | Alireza1044/albert-base-v2-wnli | [
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"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# wnli
This model is a fine-tuned version of albert-base-v2 on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898
- Accuracy: 0.5634
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
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null | null | transformers | A simple model trained on dialogues of characters in NBC series, `The Office`. The model can do a binary classification between `Michael Scott` and `Dwight Shrute`'s dialogues.
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.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
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<td class="tg-c3ow">Michael</td>
</tr>
<tr>
<td class="tg-c3ow">Label 1</td>
<td class="tg-c3ow">Dwight</td>
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</table> | {} | text-classification | Alireza1044/bert_classification_lm | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| A simple model trained on dialogues of characters in NBC series, 'The Office'. The model can do a binary classification between 'Michael Scott' and 'Dwight Shrute''s dialogues.
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<td class="tg-c3ow">Michael</td>
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<td class="tg-c3ow">Dwight</td>
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null | null | transformers |
#HarryBoy | {"tags": ["conversational"]} | text-generation | AllwynJ/HarryBoy | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#HarryBoy | [] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart50-ft-si-en
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0476
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.98 | 30 | 5.6367 |
| No log | 1.98 | 60 | 4.1221 |
| No log | 2.98 | 90 | 3.1880 |
| No log | 3.98 | 120 | 3.1175 |
| No log | 4.98 | 150 | 3.3575 |
| No log | 5.98 | 180 | 3.7855 |
| No log | 6.98 | 210 | 4.3530 |
| No log | 7.98 | 240 | 4.7216 |
| No log | 8.98 | 270 | 4.9202 |
| No log | 9.98 | 300 | 5.0476 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.6.0
- Datasets 1.11.0
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "model_index": [{"name": "mbart50-ft-si-en", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}}]}]} | text2text-generation | Aloka/mbart50-ft-si-en | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| mbart50-ft-si-en
================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.0476
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: 0.0005
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.6.0
* Datasets 1.11.0
* Tokenizers 0.10.3
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0.13212016224861145,
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0.15568953... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7272
- Matthews Correlation: 0.5343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5219 | 1.0 | 535 | 0.5340 | 0.4215 |
| 0.3467 | 2.0 | 1070 | 0.5131 | 0.5181 |
| 0.2331 | 3.0 | 1605 | 0.6406 | 0.5040 |
| 0.1695 | 4.0 | 2140 | 0.7272 | 0.5343 |
| 0.1212 | 5.0 | 2675 | 0.8399 | 0.5230 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5343023846000738, "name": "Matthews Correlation"}]}]}]} | text-classification | Alstractor/distilbert-base-uncased-finetuned-cola | [
"transformers",
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"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7272
* Matthews Correlation: 0.5343
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
| [
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null | null | transformers |
# Wav2vec2-base for Danish
This wav2vec2-base model has been pretrained on ~1300 hours of danish speech data. The pretraining data consists of podcasts and audiobooks and is unfortunately not public available. However, we were allowed to distribute the pretrained model.
This model was pretrained on 16kHz sampled speech audio. When using the model, make sure to use speech audio sampled at 16kHz.
The pre-training was done using the fairseq library in January 2021.
It needs to be fine-tuned to perform speech recognition.
# Finetuning
In order to finetune the model to speech recognition, you can draw inspiration from this [notebook tutorial](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F) or [this blog post tutorial](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2). | {"language": "da", "license": "apache-2.0", "tags": ["speech"]} | null | Alvenir/wav2vec2-base-da | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"da",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #da #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2vec2-base for Danish
This wav2vec2-base model has been pretrained on ~1300 hours of danish speech data. The pretraining data consists of podcasts and audiobooks and is unfortunately not public available. However, we were allowed to distribute the pretrained model.
This model was pretrained on 16kHz sampled speech audio. When using the model, make sure to use speech audio sampled at 16kHz.
The pre-training was done using the fairseq library in January 2021.
It needs to be fine-tuned to perform speech recognition.
# Finetuning
In order to finetune the model to speech recognition, you can draw inspiration from this notebook tutorial or this blog post tutorial. | [
"# Wav2vec2-base for Danish\nThis wav2vec2-base model has been pretrained on ~1300 hours of danish speech data. The pretraining data consists of podcasts and audiobooks and is unfortunately not public available. However, we were allowed to distribute the pretrained model.\n\nThis model was pretrained on 16kHz sampl... | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-schizophreniaReddit2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7785
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 490 | 1.8093 |
| 1.9343 | 2.0 | 980 | 1.7996 |
| 1.8856 | 3.0 | 1470 | 1.7966 |
| 1.8552 | 4.0 | 1960 | 1.7844 |
| 1.8267 | 5.0 | 2450 | 1.7839 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned-schizophreniaReddit2", "results": []}]} | fill-mask | Amalq/roberta-base-finetuned-schizophreniaReddit2 | [
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"tensorboard",
"roberta",
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"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-schizophreniaReddit2
===========================================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7785
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: 5
### Training results
### Framework versions
* Transformers 4.14.1
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
| [
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null | null | transformers |
# Question Answering NLU
Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of
training an intent classifier or a slot tagger, for example, we can ask the model intent- and
slot-related questions in natural language:
```
Context : Yes. No. I'm looking for a cheap flight to Boston.
Question: Is the user looking to book a flight?
Answer : Yes
Question: Is the user asking about departure time?
Answer : No
Question: What price is the user looking for?
Answer : cheap
Question: Where is the user flying from?
Answer : (empty)
```
Note the "Yes. No. " prepended in the context. Those are to allow the model to answer intent-related questions (e.g. "Is the user looking for a restaurant?").
Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details, please read the paper: [Language model is all you need: Natural language understanding as question answering](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf).
## Model training
Instructions for how to train and evaluate a QANLU model, as well as the necessary code for ATIS are in the [Amazon Science repository](https://github.com/amazon-research/question-answering-nlu).
## Intended use and limitations
This model has been fine-tuned on ATIS (English) and is intended to demonstrate the power of this approach. For other domains or tasks, it should be further fine-tuned
on relevant data.
## Use in transformers:
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu", use_auth_token=True)
model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu", use_auth_token=True)
qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer)
qa_input = {
'context': 'Yes. No. I want a cheap flight to Boston.',
'question': 'What is the destination?'
}
answer = qa_pipeline(qa_input)
```
## Citation
If you use this work, please cite:
```
@inproceedings{namazifar2021language,
title={Language model is all you need: Natural language understanding as question answering},
author={Namazifar, Mahdi and Papangelis, Alexandros and Tur, Gokhan and Hakkani-T{\"u}r, Dilek},
booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7803--7807},
year={2021},
organization={IEEE}
}
```
## License
This library is licensed under the CC BY NC License. | {"language": "en", "license": "cc-by-4.0", "datasets": ["atis"], "widget": [{"context": "Yes. No. I'm looking for a cheap flight to Boston."}]} | question-answering | AmazonScience/qanlu | [
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"pytorch",
"roberta",
"question-answering",
"en",
"dataset:atis",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-atis #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
# Question Answering NLU
Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of
training an intent classifier or a slot tagger, for example, we can ask the model intent- and
slot-related questions in natural language:
Note the "Yes. No. " prepended in the context. Those are to allow the model to answer intent-related questions (e.g. "Is the user looking for a restaurant?").
Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details, please read the paper: Language model is all you need: Natural language understanding as question answering.
## Model training
Instructions for how to train and evaluate a QANLU model, as well as the necessary code for ATIS are in the Amazon Science repository.
## Intended use and limitations
This model has been fine-tuned on ATIS (English) and is intended to demonstrate the power of this approach. For other domains or tasks, it should be further fine-tuned
on relevant data.
## Use in transformers:
If you use this work, please cite:
## License
This library is licensed under the CC BY NC License. | [
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null | null | transformers |
# indian-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### idli
#### kachori
#### pani puri
#### samosa
#### vada pav
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Amrrs/indian-foods | [
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"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# indian-foods
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### idli
!idli
#### kachori
!kachori
#### pani puri
!pani puri
#### samosa
!samosa
#### vada pav
!vada pav | [
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null | null | transformers |
# south-indian-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### dosai
#### idiyappam
#### idli
#### puttu
#### vadai
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Amrrs/south-indian-foods | [
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"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# south-indian-foods
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### dosai
!dosai
#### idiyappam
!idiyappam
#### idli
!idli
#### puttu
!puttu
#### vadai
!vadai | [
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0.04216891527175903,
0.09554238617420197,
0.15689313411712646,
0.1686960756778717,
0.0031046466901898384,
0.11089686304330826,
0.20598538219928741,
0.05520227923989296,
0.1809096932411... |
null | null | transformers |
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ta", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ta", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 82.94 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/drive/1-Klkgr4f-C9SanHfVC5RhP0ELUH6TYlN?usp=sharing)
| {"language": "ta", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Tamil by Amrrs", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ta", "type": "common_voice", "args": "ta"}, "metrics": [{"type": "wer", "value": 82.94, "name": "Test WER"}]}]}]} | automatic-speech-recognition | Amrrs/wav2vec2-large-xlsr-53-tamil | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ta",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ta #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
Test Result: 82.94 %
## Training
The Common Voice 'train', 'validation' datasets were used for training.
The script used for training can be found here
| [
"# Wav2Vec2-Large-XLSR-53-Tamil\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be ev... | [
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null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 479512837
- CO2 Emissions (in grams): 123.88023112815048
## Validation Metrics
- Loss: 0.6220805048942566
- Accuracy: 0.7961119332705503
- Macro F1: 0.7616345204219084
- Micro F1: 0.7961119332705503
- Weighted F1: 0.795387503907883
- Macro Precision: 0.782839455262034
- Micro Precision: 0.7961119332705503
- Weighted Precision: 0.7992606754484262
- Macro Recall: 0.7451485972167191
- Micro Recall: 0.7961119332705503
- Weighted Recall: 0.7961119332705503
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Anamika/autonlp-Feedback1-479512837
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Anamika/autonlp-Feedback1-479512837", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Anamika/autonlp-Feedback1-479512837", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | {"language": "unk", "tags": "autonlp", "datasets": ["Anamika/autonlp-data-Feedback1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 123.88023112815048} | text-classification | Anamika/autonlp-Feedback1-479512837 | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autonlp",
"unk",
"dataset:Anamika/autonlp-data-Feedback1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autonlp #unk #dataset-Anamika/autonlp-data-Feedback1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 479512837
- CO2 Emissions (in grams): 123.88023112815048
## Validation Metrics
- Loss: 0.6220805048942566
- Accuracy: 0.7961119332705503
- Macro F1: 0.7616345204219084
- Micro F1: 0.7961119332705503
- Weighted F1: 0.795387503907883
- Macro Precision: 0.782839455262034
- Micro Precision: 0.7961119332705503
- Weighted Precision: 0.7992606754484262
- Macro Recall: 0.7451485972167191
- Micro Recall: 0.7961119332705503
- Weighted Recall: 0.7961119332705503
## Usage
You can use cURL to access this model:
Or Python API:
| [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 479512837\n- CO2 Emissions (... | [
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17
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0.14138543605... |
null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 473312409
- CO2 Emissions (in grams): 25.128735714898614
## Validation Metrics
- Loss: 0.6010786890983582
- Accuracy: 0.7990650945370823
- Macro F1: 0.7429662929144928
- Micro F1: 0.7990650945370823
- Weighted F1: 0.7977660363770382
- Macro Precision: 0.7744390888231261
- Micro Precision: 0.7990650945370823
- Weighted Precision: 0.800444194278352
- Macro Recall: 0.7198278524814119
- Micro Recall: 0.7990650945370823
- Weighted Recall: 0.7990650945370823
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Anamika/autonlp-fa-473312409
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Anamika/autonlp-fa-473312409", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Anamika/autonlp-fa-473312409", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | {"language": "en", "tags": "autonlp", "datasets": ["Anamika/autonlp-data-fa"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 25.128735714898614} | text-classification | Anamika/autonlp-fa-473312409 | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:Anamika/autonlp-data-fa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-Anamika/autonlp-data-fa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 473312409
- CO2 Emissions (in grams): 25.128735714898614
## Validation Metrics
- Loss: 0.6010786890983582
- Accuracy: 0.7990650945370823
- Macro F1: 0.7429662929144928
- Micro F1: 0.7990650945370823
- Weighted F1: 0.7977660363770382
- Macro Precision: 0.7744390888231261
- Micro Precision: 0.7990650945370823
- Weighted Precision: 0.800444194278352
- Macro Recall: 0.7198278524814119
- Micro Recall: 0.7990650945370823
- Weighted Recall: 0.7990650945370823
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 473312409\n- CO2 Emissions (in grams): 25.128735714898614",
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 473312409\n- CO2 Emissions (in grams): 2... | [
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0.12656262516975403,
0.09690967947244644,
0.17336419224739075,
0.06794267892837524,
0.14914137125... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra_large_discriminator_squad2_512
This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "electra_large_discriminator_squad2_512", "results": []}]} | question-answering | Andranik/TestQA2 | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# electra_large_discriminator_squad2_512
This model is a fine-tuned version of ahotrod/electra_large_discriminator_squad2_512 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
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0.089128404855... |
null | null | transformers | This is a pretrained model that was loaded from t5-base. It has been adapted and changed by changing the max_length and summary_length. | {} | text2text-generation | AndreLiu1225/t5-news | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a pretrained model that was loaded from t5-base. It has been adapted and changed by changing the max_length and summary_length. | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
48
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# model-QA-5-epoch-RU
This model is a fine-tuned version of [AndrewChar/diplom-prod-epoch-4-datast-sber-QA](https://huggingface.co/AndrewChar/diplom-prod-epoch-4-datast-sber-QA) on sberquad
dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1991
- Validation Loss: 0.0
- Epoch: 5
## Model description
Модель отвечающая на вопрос по контектсу
это дипломная работа
## Intended uses & limitations
Контекст должен содержать не более 512 токенов
## Training and evaluation data
DataSet SberSQuAD
{'exact_match': 54.586, 'f1': 73.644}
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_re': 2e-06 'decay_steps': 2986, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.1991 | | 5 |
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"language": "ru", "tags": ["generated_from_keras_callback"], "datasets": ["sberquad"], "model-index": [{"name": "model-QA-5-epoch-RU", "results": []}]} | question-answering | AndrewChar/model-QA-5-epoch-RU | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"ru",
"dataset:sberquad",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #ru #dataset-sberquad #endpoints_compatible #region-us
| model-QA-5-epoch-RU
===================
This model is a fine-tuned version of AndrewChar/diplom-prod-epoch-4-datast-sber-QA on sberquad
dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1991
* Validation Loss: 0.0
* Epoch: 5
Model description
-----------------
Модель отвечающая на вопрос по контектсу
это дипломная работа
Intended uses & limitations
---------------------------
Контекст должен содержать не более 512 токенов
Training and evaluation data
----------------------------
DataSet SberSQuAD
{'exact\_match': 54.586, 'f1': 73.644}
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_re': 2e-06 'decay\_steps': 2986, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.15.0
* TensorFlow 2.7.0
* Datasets 1.17.0
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_re': 2e-06 'decay\\_steps': 2986, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name':... | [
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-0.0619235560297966,
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0.15098436176776886,
0.12610431015491486,
0.06211525574326515,
0.078574255108... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1355
- Wer: 0.1532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 2.5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.0826 | 0.07 | 1000 | 0.4637 | 0.4654 |
| 1.118 | 0.15 | 2000 | 0.2595 | 0.2687 |
| 1.1268 | 0.22 | 3000 | 0.2635 | 0.2661 |
| 1.0919 | 0.29 | 4000 | 0.2417 | 0.2566 |
| 1.1013 | 0.37 | 5000 | 0.2414 | 0.2567 |
| 1.0898 | 0.44 | 6000 | 0.2546 | 0.2731 |
| 1.0808 | 0.51 | 7000 | 0.2399 | 0.2535 |
| 1.0719 | 0.59 | 8000 | 0.2353 | 0.2528 |
| 1.0446 | 0.66 | 9000 | 0.2427 | 0.2545 |
| 1.0347 | 0.73 | 10000 | 0.2266 | 0.2402 |
| 1.0457 | 0.81 | 11000 | 0.2290 | 0.2448 |
| 1.0124 | 0.88 | 12000 | 0.2295 | 0.2448 |
| 1.025 | 0.95 | 13000 | 0.2138 | 0.2345 |
| 1.0107 | 1.03 | 14000 | 0.2108 | 0.2294 |
| 0.9758 | 1.1 | 15000 | 0.2019 | 0.2204 |
| 0.9547 | 1.17 | 16000 | 0.2000 | 0.2178 |
| 0.986 | 1.25 | 17000 | 0.2018 | 0.2200 |
| 0.9588 | 1.32 | 18000 | 0.1992 | 0.2138 |
| 0.9413 | 1.39 | 19000 | 0.1898 | 0.2049 |
| 0.9339 | 1.47 | 20000 | 0.1874 | 0.2056 |
| 0.9268 | 1.54 | 21000 | 0.1797 | 0.1976 |
| 0.9194 | 1.61 | 22000 | 0.1743 | 0.1905 |
| 0.8987 | 1.69 | 23000 | 0.1738 | 0.1932 |
| 0.8884 | 1.76 | 24000 | 0.1703 | 0.1873 |
| 0.8939 | 1.83 | 25000 | 0.1633 | 0.1831 |
| 0.8629 | 1.91 | 26000 | 0.1549 | 0.1750 |
| 0.8607 | 1.98 | 27000 | 0.1550 | 0.1738 |
| 0.8316 | 2.05 | 28000 | 0.1512 | 0.1709 |
| 0.8321 | 2.13 | 29000 | 0.1481 | 0.1657 |
| 0.825 | 2.2 | 30000 | 0.1446 | 0.1627 |
| 0.8115 | 2.27 | 31000 | 0.1396 | 0.1583 |
| 0.7959 | 2.35 | 32000 | 0.1389 | 0.1569 |
| 0.7835 | 2.42 | 33000 | 0.1362 | 0.1545 |
| 0.7959 | 2.49 | 34000 | 0.1355 | 0.1531 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1B-german --dataset mozilla-foundation/common_voice_8_0 --config de --split test --log_outputs
```
2. To evaluate on test dev data
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1B-german --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
``` | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "de", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - German", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "de"}, "metrics": [{"type": "wer", "value": 15.25, "name": "Test WER"}, {"type": "cer", "value": 3.78, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "de"}, "metrics": [{"type": "wer", "value": 35.29, "name": "Test WER"}, {"type": "cer", "value": 13.83, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "de"}, "metrics": [{"type": "wer", "value": 36.2, "name": "Test WER"}]}]}]} | automatic-speech-recognition | AndrewMcDowell/wav2vec2-xls-r-1B-german | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"de",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #de #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - DE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1355
* Wer: 0.1532
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 2.5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'
2. To evaluate on test dev data
| [
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0.0719810351729393,
0.07711575925350189,
0.07944095134735107,
0.112149290740... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1373
- Wer: 0.8607
## 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: 6.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.2416 | 0.84 | 500 | 1.2867 | 0.8875 |
| 2.3089 | 1.67 | 1000 | 1.8336 | 0.9548 |
| 2.3614 | 2.51 | 1500 | 1.5937 | 0.9469 |
| 2.5234 | 3.35 | 2000 | 1.9765 | 0.9867 |
| 2.5373 | 4.19 | 2500 | 1.9062 | 0.9916 |
| 2.5703 | 5.03 | 3000 | 1.9772 | 0.9915 |
| 2.4656 | 5.86 | 3500 | 1.8083 | 0.9829 |
| 2.4339 | 6.7 | 4000 | 1.7548 | 0.9752 |
| 2.344 | 7.54 | 4500 | 1.6146 | 0.9638 |
| 2.2677 | 8.38 | 5000 | 1.5105 | 0.9499 |
| 2.2074 | 9.21 | 5500 | 1.4191 | 0.9357 |
| 2.3768 | 10.05 | 6000 | 1.6663 | 0.9665 |
| 2.3804 | 10.89 | 6500 | 1.6571 | 0.9720 |
| 2.3237 | 11.72 | 7000 | 1.6049 | 0.9637 |
| 2.317 | 12.56 | 7500 | 1.5875 | 0.9655 |
| 2.2988 | 13.4 | 8000 | 1.5357 | 0.9603 |
| 2.2906 | 14.24 | 8500 | 1.5637 | 0.9592 |
| 2.2848 | 15.08 | 9000 | 1.5326 | 0.9537 |
| 2.2381 | 15.91 | 9500 | 1.5631 | 0.9508 |
| 2.2072 | 16.75 | 10000 | 1.4565 | 0.9395 |
| 2.197 | 17.59 | 10500 | 1.4304 | 0.9406 |
| 2.198 | 18.43 | 11000 | 1.4230 | 0.9382 |
| 2.1668 | 19.26 | 11500 | 1.3998 | 0.9315 |
| 2.1498 | 20.1 | 12000 | 1.3920 | 0.9258 |
| 2.1244 | 20.94 | 12500 | 1.3584 | 0.9153 |
| 2.0953 | 21.78 | 13000 | 1.3274 | 0.9054 |
| 2.0762 | 22.61 | 13500 | 1.2933 | 0.9073 |
| 2.0587 | 23.45 | 14000 | 1.2516 | 0.8944 |
| 2.0363 | 24.29 | 14500 | 1.2214 | 0.8902 |
| 2.0302 | 25.13 | 15000 | 1.2087 | 0.8871 |
| 2.0071 | 25.96 | 15500 | 1.1953 | 0.8786 |
| 1.9882 | 26.8 | 16000 | 1.1738 | 0.8712 |
| 1.9772 | 27.64 | 16500 | 1.1647 | 0.8672 |
| 1.9585 | 28.48 | 17000 | 1.1459 | 0.8635 |
| 1.944 | 29.31 | 17500 | 1.1414 | 0.8616 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"language": ["ar"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | automatic-speech-recognition | AndrewMcDowell/wav2vec2-xls-r-1b-arabic | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ar",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ar #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - AR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1373
* Wer: 0.8607
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: 6.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... | [
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-0.122341588139534,
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0.09445363283157349,
0.09529288113117218,
0.138748064637... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5500
- Wer: 1.0132
- Cer: 0.1609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.7019 | 12.65 | 1000 | 1.0510 | 0.9832 | 0.2589 |
| 1.6385 | 25.31 | 2000 | 0.6670 | 0.9915 | 0.1851 |
| 1.4344 | 37.97 | 3000 | 0.6183 | 1.0213 | 0.1797 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
``` | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "ja", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ja"}, "metrics": [{"type": "wer", "value": 95.33, "name": "Test WER"}, {"type": "cer", "value": 22.27, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "de"}, "metrics": [{"type": "wer", "value": 100.0, "name": "Test WER"}, {"type": "cer", "value": 30.33, "name": "Test CER"}, {"type": "cer", "value": 29.63, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ja"}, "metrics": [{"type": "cer", "value": 32.69, "name": "Test CER"}]}]}]} | automatic-speech-recognition | AndrewMcDowell/wav2vec2-xls-r-1b-japanese-hiragana-katakana | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"ja",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #ja #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5500
* Wer: 1.0132
* Cer: 0.1609
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'
2. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #ja #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpara... | [
97,
160,
4,
39,
66
] | [
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #ja #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperp... | [
-0.11913269758224487,
0.1495753973722458,
-0.0071907369419932365,
0.044078655540943146,
0.08584751188755035,
0.02597866952419281,
0.09281317889690399,
0.1652391403913498,
-0.055759645998477936,
0.12671718001365662,
0.06768246740102768,
0.08861856907606125,
0.0864587351679802,
0.11072776466... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4502
- Wer: 0.4783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.7972 | 0.43 | 500 | 5.1401 | 1.0 |
| 3.3241 | 0.86 | 1000 | 3.3220 | 1.0 |
| 3.1432 | 1.29 | 1500 | 3.0806 | 0.9999 |
| 2.9297 | 1.72 | 2000 | 2.5678 | 1.0057 |
| 2.2593 | 2.14 | 2500 | 1.1068 | 0.8218 |
| 2.0504 | 2.57 | 3000 | 0.7878 | 0.7114 |
| 1.937 | 3.0 | 3500 | 0.6955 | 0.6450 |
| 1.8491 | 3.43 | 4000 | 0.6452 | 0.6304 |
| 1.803 | 3.86 | 4500 | 0.5961 | 0.6042 |
| 1.7545 | 4.29 | 5000 | 0.5550 | 0.5748 |
| 1.7045 | 4.72 | 5500 | 0.5374 | 0.5743 |
| 1.6733 | 5.15 | 6000 | 0.5337 | 0.5404 |
| 1.6761 | 5.57 | 6500 | 0.5054 | 0.5266 |
| 1.655 | 6.0 | 7000 | 0.4926 | 0.5243 |
| 1.6252 | 6.43 | 7500 | 0.4946 | 0.5183 |
| 1.6209 | 6.86 | 8000 | 0.4915 | 0.5194 |
| 1.5772 | 7.29 | 8500 | 0.4725 | 0.5104 |
| 1.5602 | 7.72 | 9000 | 0.4726 | 0.5097 |
| 1.5783 | 8.15 | 9500 | 0.4667 | 0.4956 |
| 1.5442 | 8.58 | 10000 | 0.4685 | 0.4937 |
| 1.5597 | 9.01 | 10500 | 0.4708 | 0.4957 |
| 1.5406 | 9.43 | 11000 | 0.4539 | 0.4810 |
| 1.5274 | 9.86 | 11500 | 0.4502 | 0.4783 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
| {"language": ["ar"], "license": "apache-2.0", "tags": ["ar", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - Arabic", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "ar"}, "metrics": [{"type": "wer", "value": 47.54, "name": "Test WER"}, {"type": "cer", "value": 17.64, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ar"}, "metrics": [{"type": "wer", "value": 93.72, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ar"}, "metrics": [{"type": "wer", "value": 92.49, "name": "Test WER"}]}]}]} | automatic-speech-recognition | AndrewMcDowell/wav2vec2-xls-r-300m-arabic | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - AR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4502
* Wer: 0.4783
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ar #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
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0.07449441403150558,
0.0982985720038414,
0.07953846454620361,
0.092545419931411... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.
eval results:
WER: 0.20161578657865786
CER: 0.05062357805269733
-->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1768
- Wer: 0.2016
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.7531 | 0.04 | 500 | 5.4564 | 1.0 |
| 2.9882 | 0.08 | 1000 | 3.0041 | 1.0 |
| 2.1953 | 0.13 | 1500 | 1.1723 | 0.7121 |
| 1.2406 | 0.17 | 2000 | 0.3656 | 0.3623 |
| 1.1294 | 0.21 | 2500 | 0.2843 | 0.2926 |
| 1.0731 | 0.25 | 3000 | 0.2554 | 0.2664 |
| 1.051 | 0.3 | 3500 | 0.2387 | 0.2535 |
| 1.0479 | 0.34 | 4000 | 0.2345 | 0.2512 |
| 1.0026 | 0.38 | 4500 | 0.2270 | 0.2452 |
| 0.9921 | 0.42 | 5000 | 0.2212 | 0.2353 |
| 0.9839 | 0.47 | 5500 | 0.2141 | 0.2330 |
| 0.9907 | 0.51 | 6000 | 0.2122 | 0.2334 |
| 0.9788 | 0.55 | 6500 | 0.2114 | 0.2270 |
| 0.9687 | 0.59 | 7000 | 0.2066 | 0.2323 |
| 0.9777 | 0.64 | 7500 | 0.2033 | 0.2237 |
| 0.9476 | 0.68 | 8000 | 0.2020 | 0.2194 |
| 0.9625 | 0.72 | 8500 | 0.1977 | 0.2191 |
| 0.9497 | 0.76 | 9000 | 0.1976 | 0.2175 |
| 0.9781 | 0.81 | 9500 | 0.1956 | 0.2159 |
| 0.9552 | 0.85 | 10000 | 0.1958 | 0.2191 |
| 0.9345 | 0.89 | 10500 | 0.1964 | 0.2158 |
| 0.9528 | 0.93 | 11000 | 0.1926 | 0.2154 |
| 0.9502 | 0.98 | 11500 | 0.1953 | 0.2149 |
| 0.9358 | 1.02 | 12000 | 0.1927 | 0.2167 |
| 0.941 | 1.06 | 12500 | 0.1901 | 0.2115 |
| 0.9287 | 1.1 | 13000 | 0.1936 | 0.2090 |
| 0.9491 | 1.15 | 13500 | 0.1900 | 0.2104 |
| 0.9478 | 1.19 | 14000 | 0.1931 | 0.2120 |
| 0.946 | 1.23 | 14500 | 0.1914 | 0.2134 |
| 0.9499 | 1.27 | 15000 | 0.1931 | 0.2173 |
| 0.9346 | 1.32 | 15500 | 0.1913 | 0.2105 |
| 0.9509 | 1.36 | 16000 | 0.1902 | 0.2137 |
| 0.9294 | 1.4 | 16500 | 0.1895 | 0.2086 |
| 0.9418 | 1.44 | 17000 | 0.1913 | 0.2183 |
| 0.9302 | 1.49 | 17500 | 0.1884 | 0.2114 |
| 0.9418 | 1.53 | 18000 | 0.1894 | 0.2108 |
| 0.9363 | 1.57 | 18500 | 0.1886 | 0.2132 |
| 0.9338 | 1.61 | 19000 | 0.1856 | 0.2078 |
| 0.9185 | 1.66 | 19500 | 0.1852 | 0.2056 |
| 0.9216 | 1.7 | 20000 | 0.1874 | 0.2095 |
| 0.9176 | 1.74 | 20500 | 0.1873 | 0.2078 |
| 0.9288 | 1.78 | 21000 | 0.1865 | 0.2097 |
| 0.9278 | 1.83 | 21500 | 0.1869 | 0.2100 |
| 0.9295 | 1.87 | 22000 | 0.1878 | 0.2095 |
| 0.9221 | 1.91 | 22500 | 0.1852 | 0.2121 |
| 0.924 | 1.95 | 23000 | 0.1855 | 0.2042 |
| 0.9104 | 2.0 | 23500 | 0.1858 | 0.2105 |
| 0.9284 | 2.04 | 24000 | 0.1850 | 0.2080 |
| 0.9162 | 2.08 | 24500 | 0.1839 | 0.2045 |
| 0.9111 | 2.12 | 25000 | 0.1838 | 0.2080 |
| 0.91 | 2.17 | 25500 | 0.1889 | 0.2106 |
| 0.9152 | 2.21 | 26000 | 0.1856 | 0.2026 |
| 0.9209 | 2.25 | 26500 | 0.1891 | 0.2133 |
| 0.9094 | 2.29 | 27000 | 0.1857 | 0.2089 |
| 0.9065 | 2.34 | 27500 | 0.1840 | 0.2052 |
| 0.9156 | 2.38 | 28000 | 0.1833 | 0.2062 |
| 0.8986 | 2.42 | 28500 | 0.1789 | 0.2001 |
| 0.9045 | 2.46 | 29000 | 0.1769 | 0.2022 |
| 0.9039 | 2.51 | 29500 | 0.1819 | 0.2073 |
| 0.9145 | 2.55 | 30000 | 0.1828 | 0.2063 |
| 0.9081 | 2.59 | 30500 | 0.1811 | 0.2049 |
| 0.9252 | 2.63 | 31000 | 0.1833 | 0.2086 |
| 0.8957 | 2.68 | 31500 | 0.1795 | 0.2083 |
| 0.891 | 2.72 | 32000 | 0.1809 | 0.2058 |
| 0.9023 | 2.76 | 32500 | 0.1812 | 0.2061 |
| 0.8918 | 2.8 | 33000 | 0.1775 | 0.1997 |
| 0.8852 | 2.85 | 33500 | 0.1790 | 0.1997 |
| 0.8928 | 2.89 | 34000 | 0.1767 | 0.2013 |
| 0.9079 | 2.93 | 34500 | 0.1735 | 0.1986 |
| 0.9032 | 2.97 | 35000 | 0.1793 | 0.2024 |
| 0.9018 | 3.02 | 35500 | 0.1778 | 0.2027 |
| 0.8846 | 3.06 | 36000 | 0.1776 | 0.2046 |
| 0.8848 | 3.1 | 36500 | 0.1812 | 0.2064 |
| 0.9062 | 3.14 | 37000 | 0.1800 | 0.2018 |
| 0.9011 | 3.19 | 37500 | 0.1783 | 0.2049 |
| 0.8996 | 3.23 | 38000 | 0.1810 | 0.2036 |
| 0.893 | 3.27 | 38500 | 0.1805 | 0.2056 |
| 0.897 | 3.31 | 39000 | 0.1773 | 0.2035 |
| 0.8992 | 3.36 | 39500 | 0.1804 | 0.2054 |
| 0.8987 | 3.4 | 40000 | 0.1768 | 0.2016 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-german-de --dataset mozilla-foundation/common_voice_7_0 --config de --split test --log_outputs
```
2. To evaluate on test dev data
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-german-de --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
``` | {"language": ["de"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "de", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - German", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "de"}, "metrics": [{"type": "wer", "value": 20.16, "name": "Test WER"}, {"type": "cer", "value": 5.06, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "de"}, "metrics": [{"type": "wer", "value": 39.79, "name": "Test WER"}, {"type": "cer", "value": 15.02, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "de"}, "metrics": [{"type": "wer", "value": 47.95, "name": "Test WER"}]}]}]} | automatic-speech-recognition | AndrewMcDowell/wav2vec2-xls-r-300m-german-de | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - DE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1768
* Wer: 0.2016
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 3.4
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test'
2. To evaluate on test dev data
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
111,
159,
4,
41,
44
] | [
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #de #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyper... | [
-0.12741737067699432,
0.11049387603998184,
-0.006292941980063915,
0.045448631048202515,
0.1109750047326088,
0.027023954316973686,
0.09660723805427551,
0.1629907190799713,
-0.066348597407341,
0.10548499971628189,
0.06490739434957504,
0.09815691411495209,
0.08025956898927689,
0.1042460799217... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable.
On mozilla-foundation/common_voice_8_0 it achieved:
- cer: 23.64%
On speech-recognition-community-v2/dev_data it achieved:
- cer: 30.99%
It achieves the following results on the evaluation set:
- Loss: 0.5212
- Wer: 1.3068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0974 | 4.72 | 1000 | 4.0178 | 1.9535 |
| 2.1276 | 9.43 | 2000 | 0.9301 | 1.2128 |
| 1.7622 | 14.15 | 3000 | 0.7103 | 1.5527 |
| 1.6397 | 18.87 | 4000 | 0.6729 | 1.4269 |
| 1.5468 | 23.58 | 5000 | 0.6087 | 1.2497 |
| 1.4885 | 28.3 | 6000 | 0.5786 | 1.3222 |
| 1.451 | 33.02 | 7000 | 0.5726 | 1.3768 |
| 1.3912 | 37.74 | 8000 | 0.5518 | 1.2497 |
| 1.3617 | 42.45 | 9000 | 0.5352 | 1.2694 |
| 1.3113 | 47.17 | 10000 | 0.5228 | 1.2781 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
``` | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "ja", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300-m", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ja"}, "metrics": [{"type": "wer", "value": 95.82, "name": "Test WER"}, {"type": "cer", "value": 23.64, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "de"}, "metrics": [{"type": "wer", "value": 100.0, "name": "Test WER"}, {"type": "cer", "value": 30.99, "name": "Test CER"}, {"type": "cer", "value": 30.37, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ja"}, "metrics": [{"type": "cer", "value": 34.42, "name": "Test CER"}]}]}]} | automatic-speech-recognition | AndrewMcDowell/wav2vec2-xls-r-300m-japanese | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | 2022-03-02T23:29:04+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - JA dataset.
Kanji are converted into Hiragana using the pykakasi library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable.
On mozilla-foundation/common\_voice\_8\_0 it achieved:
* cer: 23.64%
On speech-recognition-community-v2/dev\_data it achieved:
* cer: 30.99%
It achieves the following results on the evaluation set:
* Loss: 0.5212
* Wer: 1.3068
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 48
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'
2. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test'
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... | [
111,
132,
4,
39,
66
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-0.1087740883231163,
0.1477416604757309,
-0.005788300186395645,
0.013155115768313408,
0.10168784856796265,
0.03503540903329849,
0.11511356383562088,
0.15956926345825195,
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0.13984927535057068,
0.06451325118541718,
0.10002699494361877,
0.09042244404554367,
0.122649326920... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbert-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1264
- Precision: 0.9305
- Recall: 0.9375
- F1: 0.9340
- Accuracy: 0.9700
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.301 | 1.0 | 625 | 0.1756 | 0.8843 | 0.9067 | 0.8953 | 0.9500 |
| 0.1259 | 2.0 | 1250 | 0.1248 | 0.9285 | 0.9335 | 0.9310 | 0.9688 |
| 0.0895 | 3.0 | 1875 | 0.1264 | 0.9305 | 0.9375 | 0.9340 | 0.9700 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mbert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "lv"}, "metrics": [{"type": "precision", "value": 0.9304986338797814, "name": "Precision"}, {"type": "recall", "value": 0.9375430144528561, "name": "Recall"}, {"type": "f1", "value": 0.9340075419952005, "name": "F1"}, {"type": "accuracy", "value": 0.9699674740348558, "name": "Accuracy"}]}]}]} | token-classification | Andrey1989/mbert-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| mbert-finetuned-ner
===================
This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1264
* Precision: 0.9305
* Recall: 0.9375
* F1: 0.9340
* Accuracy: 0.9700
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... | [
66,
98,
4,
35
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... | [
-0.10305804759263992,
0.10961544513702393,
-0.0018086218042299151,
0.1267901211977005,
0.1558895707130432,
0.03104560635983944,
0.11313606053590775,
0.12545624375343323,
-0.08956429362297058,
0.018652750179171562,
0.1335294246673584,
0.1636468470096588,
0.013856463134288788,
0.110811509191... |
null | null | transformers | This model is a finetuning of bert-base-greek-uncased as a Token Classifier which predicts at each token which punctuation mark it is followed by. The model preprocesses everything to lowercase and removes all Greek diacritics. For information on pretraining of the Greek Bert model, please refer to [Greek Bert](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1)
# Finetuning Parameters
Epochs: 5
Maximum Sequence Length: 512
Learning Rate: 4e−5
Batch Size: 16
Finetuning Data:
Greek Europarl data available at: https://opus.nlpl.eu/Europarl.php
Tokens: 44.1M
Sentences: 1.6M
Punctuation Points Recognised:
'.' (0) : Full stop
',' (1) : Comma
';' (2) : Greek question mark
'-' (3) : Dash
':' (4) : Semicolon
'0' (5) : No punctuation point is following
# Load Finetuned Model
~~~
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Andrianos/bert-base-greek-punctuation-prediction-finetuned")
model = AutoModelForTokenClassification.from_pretrained("Andrianos/bert-base-greek-punctuation-prediction-finetuned")
~~~
# Using the Model
If you are interested in trying out examples and finding the limitations of the model, the starter Python code to use the model is available at [Github Repo](https://github.com/Andrian0s/Greek-Transformer-Model-Punctuation-Prediction)
# Examples of the Model
Using the demo script, we tried out a few brief examples and show the results below
Input | Input with Predictions
------------- | -------------
"προσεκτικά στον δρομο θα σε περιμενω" | "προσεκτικα στον δρομο, θα σε περιμενω"
"τι θα φας για βραδινο" | "τι θα φας για βραδινο;"
"κυριε μαυροκέφαλε εσπασε η κεραια του διαδικτυου θα παρω τηλεφωνο την cyta" | "κυριε μαυροκεφαλε, εσπασε η κεραια του διαδικτυου. θα παρω τηλεφωνο την cyta."
"κυριε μαυροκεφαλε εσπασεν η αντεννα του ιντερνετ εννα πιαω τηλεφωνον την cyta" | "κυριε μαυροκεφαλε, εσπασεν η αντεννα του ιντερνετ. εννα πιαω τηλεφωνον την cyta."
The last two examples have identical meanings, the first is written in plain Modern Greek and the latter in the Cypriot Dialect. It is interesting to see the model performs similarly, even if some words and suffixes are out of vocabulary.
# Further Performance Improvements
We would be happy to hear people have finetuned this model with more and diverse datasets, as we expect this to increase robustness.
Within our research, improvements to consistency in punctuation prediction have shown to be possible with techniques such as sliding windows (during inference) for larger documents, weighted loss and ensembling of different models. Make sure to cite our work when you further our models with the aforementioned techniques.
# Author
This model is further work based on the winning submission at Shared Task 2 Sentence End and Punctuation Prediction in NLG Text at SwissText2021.
The winning submission is entitled "UZH OnPoint at Swisstext-2021: Sentence End and Punctuation Prediction in NLG Text Through Ensembling of Different Transformers" in the Proceedings of the 6th SwissText Held Online. It is publicly available at http://ceur-ws.org/Vol-2957/sepp_paper2.pdf
If you use the model, please cite the following:
@inproceedings{ST2021-OnPoint,
title={UZH OnPoint at Swisstext-2021: Sentence End and Punctuation Prediction in NLG Text Through Ensembling of Different Transformers},
author={Michail, Andrianos and Wehrli, Silvan and Bucková, Terézia},
booktitle={Proceedings of the 1st Shared Task on Sentence
End and Punctuation Prediction in NLG Text (SEPPNLG 2021) at SwissText 2021},
year={2021}
}
Model Finetuned and released by Andrianos Michail with resources provided by [Department of Computational Linguistics, University of Zurich](https://www.cl.uzh.ch/en.html)
| Github: [@Andrian0s](https://github.com/Andrian0s) | LinkedIn: [amichail2](https://www.linkedin.com/in/amichail2/) | {} | token-classification | Andrianos/bert-base-greek-punctuation-prediction-finetuned | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is a finetuning of bert-base-greek-uncased as a Token Classifier which predicts at each token which punctuation mark it is followed by. The model preprocesses everything to lowercase and removes all Greek diacritics. For information on pretraining of the Greek Bert model, please refer to Greek Bert
Finetuning Parameters
=====================
Epochs: 5
Maximum Sequence Length: 512
Learning Rate: 4e−5
Batch Size: 16
Finetuning Data:
Greek Europarl data available at: URL
Tokens: 44.1M
Sentences: 1.6M
Punctuation Points Recognised:
'.' (0) : Full stop
',' (1) : Comma
';' (2) : Greek question mark
'-' (3) : Dash
':' (4) : Semicolon
'0' (5) : No punctuation point is following
Load Finetuned Model
====================
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Andrianos/bert-base-greek-punctuation-prediction-finetuned")
model = AutoModelForTokenClassification.from_pretrained("Andrianos/bert-base-greek-punctuation-prediction-finetuned")
```
Using the Model
===============
If you are interested in trying out examples and finding the limitations of the model, the starter Python code to use the model is available at Github Repo
Examples of the Model
=====================
Using the demo script, we tried out a few brief examples and show the results below
The last two examples have identical meanings, the first is written in plain Modern Greek and the latter in the Cypriot Dialect. It is interesting to see the model performs similarly, even if some words and suffixes are out of vocabulary.
Further Performance Improvements
================================
We would be happy to hear people have finetuned this model with more and diverse datasets, as we expect this to increase robustness.
Within our research, improvements to consistency in punctuation prediction have shown to be possible with techniques such as sliding windows (during inference) for larger documents, weighted loss and ensembling of different models. Make sure to cite our work when you further our models with the aforementioned techniques.
Author
======
This model is further work based on the winning submission at Shared Task 2 Sentence End and Punctuation Prediction in NLG Text at SwissText2021.
The winning submission is entitled "UZH OnPoint at Swisstext-2021: Sentence End and Punctuation Prediction in NLG Text Through Ensembling of Different Transformers" in the Proceedings of the 6th SwissText Held Online. It is publicly available at URL
If you use the model, please cite the following:
@inproceedings{ST2021-OnPoint,
title={UZH OnPoint at Swisstext-2021: Sentence End and Punctuation Prediction in NLG Text Through Ensembling of Different Transformers},
author={Michail, Andrianos and Wehrli, Silvan and Bucková, Terézia},
booktitle={Proceedings of the 1st Shared Task on Sentence
End and Punctuation Prediction in NLG Text (SEPPNLG 2021) at SwissText 2021},
year={2021}
}
Model Finetuned and released by Andrianos Michail with resources provided by Department of Computational Linguistics, University of Zurich
| Github: @Andrian0s | LinkedIn: amichail2
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
37
] | [
"passage: TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.04952388256788254,
0.052763525396585464,
-0.008742042817175388,
0.033980391919612885,
0.16650345921516418,
0.031232766807079315,
0.056794650852680206,
0.08634597808122635,
0.05724777653813362,
-0.022096728906035423,
0.12041265517473221,
0.25661665201187134,
-0.04172574356198311,
0.09726... |
null | null | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person's name right after another person's name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person's name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | token-classification | Andrija/M-bert-NER | [
"transformers",
"pytorch",
"bert",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #bert #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
61
] | [
"passage: TAGS\n#transformers #pytorch #bert #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.08848606795072556,
0.10466885566711426,
-0.005547394044697285,
0.05923883244395256,
0.11184278875589371,
0.043874822556972504,
0.1225629448890686,
0.12288068979978561,
0.031205151230096817,
-0.06936771422624588,
0.1197349950671196,
0.20268696546554565,
0.007789667695760727,
0.0566231049... |
null | null | null | from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
tokenizer.decode(encoded['input_ids'])
# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s> | {} | null | Andrija/RobertaFastBPE | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
URL(encoded['input_ids'])
# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s> | [
"# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\nURL(encoded['input_ids'])",
"# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>"
] | [
"TAGS\n#region-us \n",
"# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\nURL(encoded['input_ids'])",
"# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>"
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6,
101,
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0.0023704604245722294,
0.06820507347583771,
0.03503307327... |
null | null | transformers | # Transformer language model for Croatian and Serbian
Trained on 43GB datasets that contain Croatian and Serbian language for one epochs (9.6 mil. steps, 3 epochs).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
Validation number of exampels run for perplexity:1620487 sentences
Perplexity:6.02
Start loss: 8.6
Final loss: 2.0
Thoughts: Model could be trained more, the training did not stagnate.
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-F` | 80M | Fifth | Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr (43 GB of text) | | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "srwac", "leipzig", "cc100", "hrwac"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | fill-mask | Andrija/SRoBERTa-F | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 43GB datasets that contain Croatian and Serbian language for one epochs (9.6 mil. steps, 3 epochs).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
Validation number of exampels run for perplexity:1620487 sentences
Perplexity:6.02
Start loss: 8.6
Final loss: 2.0
Thoughts: Model could be trained more, the training did not stagnate.
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
96
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] | [
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null | null | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | token-classification | Andrija/SRoBERTa-L-NER | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
62
] | [
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0.052055455... |
null | null | transformers | # Transformer language model for Croatian and Serbian
Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps).
Leipzig, OSCAR and srWac datasets
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-L` | 80M | Third | Leipzig Corpus, OSCAR and srWac (6 GB of text) | | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "srwac", "leipzig"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | fill-mask | Andrija/SRoBERTa-L | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps).
Leipzig, OSCAR and srWac datasets
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
79
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] | [
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0.1550208479166031,
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0.093564242124... |
null | null | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | token-classification | Andrija/SRoBERTa-NER | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
62
] | [
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0.2130488008260727,
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0.052055455... |
null | null | transformers |
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person's name right after another person's name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person's name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | token-classification | Andrija/SRoBERTa-XL-NER | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
62
] | [
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0.12124678492546082,
0.2130488008260727,
0.005292007699608803,
0.052055455... |
null | null | transformers |
# Transformer language model for Croatian and Serbian
Trained on 28GB datasets that contain Croatian and Serbian language for one epochs (3 mil. steps).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-XL` | 80M | Forth | Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr (28 GB of text) | | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "srwac", "leipzig", "cc100", "hrwac"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | fill-mask | Andrija/SRoBERTa-XL | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"dataset:cc100",
"dataset:hrwac",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 28GB datasets that contain Croatian and Serbian language for one epochs (3 mil. steps).
Leipzig Corpus, OSCAR, srWac, hrWac, cc100-hr and cc100-sr datasets
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-srwac #dataset-leipzig #dataset-cc100 #dataset-hrwac #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
92
] | [
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0.1612124890089035,
0.13867789506912231,
0.020428461953997612,
0.104291953146... |
null | null | transformers | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MIS | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
B-DERIV-PER| Begginning derivative that describes relation to a person
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "datasets": ["hr500k"], "widget": [{"text": "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"}]} | token-classification | Andrija/SRoBERTa-base-NER | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
| [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
62
] | [
"passage: TAGS\n#transformers #pytorch #roberta #token-classification #hr #sr #multilingual #dataset-hr500k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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-0.07109397649765015,
0.12124678492546082,
0.2130488008260727,
0.005292007699608803,
0.052055455... |
null | null | transformers | # Transformer language model for Croatian and Serbian
Trained on 3GB datasets that contain Croatian and Serbian language for two epochs.
Leipzig and OSCAR datasets
# Information of dataset
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa-base` | 80M | Second | Leipzig Corpus and OSCAR (3 GB of text) | | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["oscar", "leipzig"], "widget": [{"text": "Ovo je po\u010detak <mask>."}]} | fill-mask | Andrija/SRoBERTa-base | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 3GB datasets that contain Croatian and Serbian language for two epochs.
Leipzig and OSCAR datasets
Information of dataset
======================
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-oscar #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
72
] | [
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] | [
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0.09971416741... |
null | null | transformers | # Transformer language model for Croatian and Serbian
Trained on 0.7GB dataset Croatian and Serbian language for one epoch.
Dataset from Leipzig Corpora.
# Information of dataset
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `Andrija/SRoBERTa` | 120M | First | Leipzig Corpus (0.7 GB of text) |
# How to use in code
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Andrija/SRoBERTa")
model = AutoModelForMaskedLM.from_pretrained("Andrija/SRoBERTa")
``` | {"language": ["hr", "sr", "multilingual"], "license": "apache-2.0", "tags": ["masked-lm"], "datasets": ["leipzig"], "widget": [{"text": "Gde je <mask>."}]} | fill-mask | Andrija/SRoBERTa | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"hr",
"sr",
"multilingual",
"dataset:leipzig",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"hr",
"sr",
"multilingual"
] | TAGS
#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Transformer language model for Croatian and Serbian
===================================================
Trained on 0.7GB dataset Croatian and Serbian language for one epoch.
Dataset from Leipzig Corpora.
Information of dataset
======================
How to use in code
==================
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
66
] | [
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #masked-lm #hr #sr #multilingual #dataset-leipzig #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | null | C:\Users\andry\Desktop\Выжигание 24-12-2021.jpg | {} | null | Andry/1111 | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
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null | null | null |
Now we only upload two models for creating demos for image and video classification.
More models and code can be found in our github repo: [UniFormer](https://github.com/Sense-X/UniFormer). | {"license": "mit"} | null | Andy1621/uniformer | [
"license:mit",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#license-mit #has_space #region-us
|
Now we only upload two models for creating demos for image and video classification.
More models and code can be found in our github repo: UniFormer. | [] | [
"TAGS\n#license-mit #has_space #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0609
- Precision: 0.9275
- Recall: 0.9365
- F1: 0.9320
- Accuracy: 0.9840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2527 | 1.0 | 878 | 0.0706 | 0.9120 | 0.9181 | 0.9150 | 0.9803 |
| 0.0517 | 2.0 | 1756 | 0.0603 | 0.9174 | 0.9349 | 0.9261 | 0.9830 |
| 0.031 | 3.0 | 2634 | 0.0609 | 0.9275 | 0.9365 | 0.9320 | 0.9840 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.984018301110458}}]}]} | token-classification | Ann2020/distilbert-base-uncased-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0609
* Precision: 0.9275
* Recall: 0.9365
* F1: 0.9320
* Accuracy: 0.9840
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
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null | null | transformers | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on bert-base-uncased model and pre-trained for text input | {} | feature-extraction | Anonymous/ReasonBERT-BERT | [
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"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see URL
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null | null | transformers | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on roberta-base model and pre-trained for text input | {} | feature-extraction | Anonymous/ReasonBERT-RoBERTa | [
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"region:us"
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| Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see URL
This is based on roberta-base model and pre-trained for text input | [] | [
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null | null | transformers | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on tapas-base(no_reset) model and pre-trained for table input | {} | feature-extraction | Anonymous/ReasonBERT-TAPAS | [
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| Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see URL
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null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20384195
- CO2 Emissions (in grams): 4.214012748213151
## Validation Metrics
- Loss: 1.0120062828063965
- Rouge1: 41.1808
- Rouge2: 26.2564
- RougeL: 31.3106
- RougeLsum: 38.9991
- Gen Len: 58.45
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/Anorak/autonlp-Niravana-test2-20384195
``` | {"language": "unk", "tags": "autonlp", "datasets": ["Anorak/autonlp-data-Niravana-test2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 4.214012748213151} | text2text-generation | Anorak/nirvana | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:Anorak/autonlp-data-Niravana-test2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20384195
- CO2 Emissions (in grams): 4.214012748213151
## Validation Metrics
- Loss: 1.0120062828063965
- Rouge1: 41.1808
- Rouge2: 26.2564
- RougeL: 31.3106
- RougeLsum: 38.9991
- Gen Len: 58.45
## Usage
You can use cURL to access this model:
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# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | text-generation | AnthonyNelson/DialoGPT-small-ricksanchez | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Anthos23/distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0662
- Validation Loss: 0.2623
- Train Accuracy: 0.9083
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 21045, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2101 | 0.2373 | 0.9083 | 0 |
| 0.1065 | 0.2645 | 0.9060 | 1 |
| 0.0662 | 0.2623 | 0.9083 | 2 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.5.0
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Anthos23/distilbert-base-uncased-finetuned-sst2", "results": []}]} | text-classification | Anthos23/distilbert-base-uncased-finetuned-sst2 | [
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"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Anthos23/distilbert-base-uncased-finetuned-sst2
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0662
* Validation Loss: 0.2623
* Train Accuracy: 0.9083
* Epoch: 2
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:
* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 21045, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* TensorFlow 2.5.0
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
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null | null | transformers |
# Jordan DialoGPT Model | {"tags": ["conversational"]} | text-generation | Apisate/DialoGPT-small-jordan | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models | {"language": "en", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "thumbnail": "Keywords to Sentences"} | text2text-generation | Apoorva/k2t-test | [
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"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
65
] | [
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
0.048417139798402786,
0.0009807678870856762,
-0.007233020383864641,
0.0036215882282704115,
0.13428790867328644,
0.017067421227693558,
0.10295163840055466,
0.1634819209575653,
-0.01886797696352005,
-0.03132914751768112,
0.13148918747901917,
0.2336915284395218,
0.004890760872513056,
0.083379... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-ner
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0700
- Precision: 0.9301
- Recall: 0.9376
- F1: 0.9338
- Accuracy: 0.9852
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.096 | 1.0 | 1756 | 0.0752 | 0.9163 | 0.9201 | 0.9182 | 0.9811 |
| 0.0481 | 2.0 | 3512 | 0.0761 | 0.9169 | 0.9293 | 0.9231 | 0.9830 |
| 0.0251 | 3.0 | 5268 | 0.0700 | 0.9301 | 0.9376 | 0.9338 | 0.9852 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9301181102362205, "name": "Precision"}, {"type": "recall", "value": 0.9376033513394334, "name": "Recall"}, {"type": "f1", "value": 0.9338457315399397, "name": "F1"}, {"type": "accuracy", "value": 0.9851613086447802, "name": "Accuracy"}]}]}]} | token-classification | ArBert/albert-base-v2-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-finetuned-ner
============================
This model is a fine-tuned version of albert-base-v2 on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0700
* Precision: 0.9301
* Recall: 0.9376
* F1: 0.9338
* Accuracy: 0.9852
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
### Framework versions
* Transformers 4.14.1
* Pytorch 1.10.1
* Datasets 1.17.0
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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0.013222930021584034,
0.107548505... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner-kmeans
This model is a fine-tuned version of [ArBert/bert-base-uncased-finetuned-ner](https://huggingface.co/ArBert/bert-base-uncased-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1169
- Precision: 0.9084
- Recall: 0.9245
- F1: 0.9164
- Accuracy: 0.9792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.036 | 1.0 | 1123 | 0.1010 | 0.9086 | 0.9117 | 0.9101 | 0.9779 |
| 0.0214 | 2.0 | 2246 | 0.1094 | 0.9033 | 0.9199 | 0.9115 | 0.9784 |
| 0.014 | 3.0 | 3369 | 0.1169 | 0.9084 | 0.9245 | 0.9164 | 0.9792 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner-kmeans", "results": []}]} | token-classification | ArBert/bert-base-uncased-finetuned-ner-kmeans | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-ner-kmeans
======================================
This model is a fine-tuned version of ArBert/bert-base-uncased-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1169
* Precision: 0.9084
* Recall: 0.9245
* F1: 0.9164
* Accuracy: 0.9792
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
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-0.10436924546957016,
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0.12480532377958298,
0.189627543091774,
0.002304124180227518,
0.1098845824... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
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: 0.0905
- Precision: 0.9068
- Recall: 0.9200
- F1: 0.9133
- Accuracy: 0.9787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1266 | 1.0 | 1123 | 0.0952 | 0.8939 | 0.8869 | 0.8904 | 0.9742 |
| 0.0741 | 2.0 | 2246 | 0.0866 | 0.8936 | 0.9247 | 0.9089 | 0.9774 |
| 0.0496 | 3.0 | 3369 | 0.0905 | 0.9068 | 0.9200 | 0.9133 | 0.9787 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner", "results": []}]} | token-classification | ArBert/bert-base-uncased-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0905
* Precision: 0.9068
* Recall: 0.9200
* F1: 0.9133
* Accuracy: 0.9787
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
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-0.10436924546957016,
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner-agglo-twitter
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Precision: 0.6885
- Recall: 0.7665
- F1: 0.7254
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 245 | 0.2820 | 0.6027 | 0.7543 | 0.6700 |
| No log | 2.0 | 490 | 0.2744 | 0.6308 | 0.7864 | 0.7000 |
| 0.2301 | 3.0 | 735 | 0.2788 | 0.6433 | 0.7637 | 0.6984 |
| 0.2301 | 4.0 | 980 | 0.3255 | 0.6834 | 0.7221 | 0.7022 |
| 0.1153 | 5.0 | 1225 | 0.3453 | 0.6686 | 0.7439 | 0.7043 |
| 0.1153 | 6.0 | 1470 | 0.3988 | 0.6797 | 0.7420 | 0.7094 |
| 0.0617 | 7.0 | 1715 | 0.4711 | 0.6702 | 0.7259 | 0.6969 |
| 0.0617 | 8.0 | 1960 | 0.4904 | 0.6904 | 0.7505 | 0.7192 |
| 0.0328 | 9.0 | 2205 | 0.5088 | 0.6591 | 0.7713 | 0.7108 |
| 0.0328 | 10.0 | 2450 | 0.5709 | 0.6468 | 0.7788 | 0.7067 |
| 0.019 | 11.0 | 2695 | 0.5570 | 0.6642 | 0.7533 | 0.7059 |
| 0.019 | 12.0 | 2940 | 0.5574 | 0.6899 | 0.7656 | 0.7258 |
| 0.0131 | 13.0 | 3185 | 0.5858 | 0.6952 | 0.7609 | 0.7265 |
| 0.0131 | 14.0 | 3430 | 0.6239 | 0.6556 | 0.7826 | 0.7135 |
| 0.0074 | 15.0 | 3675 | 0.5931 | 0.6825 | 0.7599 | 0.7191 |
| 0.0074 | 16.0 | 3920 | 0.6364 | 0.6785 | 0.7580 | 0.7161 |
| 0.005 | 17.0 | 4165 | 0.6437 | 0.6855 | 0.7580 | 0.7199 |
| 0.005 | 18.0 | 4410 | 0.6610 | 0.6779 | 0.7599 | 0.7166 |
| 0.0029 | 19.0 | 4655 | 0.6625 | 0.6853 | 0.7656 | 0.7232 |
| 0.0029 | 20.0 | 4900 | 0.6645 | 0.6885 | 0.7665 | 0.7254 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-ner-agglo-twitter", "results": []}]} | token-classification | ArBert/roberta-base-finetuned-ner-agglo-twitter | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner-agglo-twitter
========================================
This model is a fine-tuned version of ArBert/roberta-base-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6645
* Precision: 0.6885
* Recall: 0.7665
* F1: 0.7254
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: 20
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner-kmeans-twitter
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Precision: 0.6885
- Recall: 0.7665
- F1: 0.7254
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 245 | 0.2820 | 0.6027 | 0.7543 | 0.6700 |
| No log | 2.0 | 490 | 0.2744 | 0.6308 | 0.7864 | 0.7000 |
| 0.2301 | 3.0 | 735 | 0.2788 | 0.6433 | 0.7637 | 0.6984 |
| 0.2301 | 4.0 | 980 | 0.3255 | 0.6834 | 0.7221 | 0.7022 |
| 0.1153 | 5.0 | 1225 | 0.3453 | 0.6686 | 0.7439 | 0.7043 |
| 0.1153 | 6.0 | 1470 | 0.3988 | 0.6797 | 0.7420 | 0.7094 |
| 0.0617 | 7.0 | 1715 | 0.4711 | 0.6702 | 0.7259 | 0.6969 |
| 0.0617 | 8.0 | 1960 | 0.4904 | 0.6904 | 0.7505 | 0.7192 |
| 0.0328 | 9.0 | 2205 | 0.5088 | 0.6591 | 0.7713 | 0.7108 |
| 0.0328 | 10.0 | 2450 | 0.5709 | 0.6468 | 0.7788 | 0.7067 |
| 0.019 | 11.0 | 2695 | 0.5570 | 0.6642 | 0.7533 | 0.7059 |
| 0.019 | 12.0 | 2940 | 0.5574 | 0.6899 | 0.7656 | 0.7258 |
| 0.0131 | 13.0 | 3185 | 0.5858 | 0.6952 | 0.7609 | 0.7265 |
| 0.0131 | 14.0 | 3430 | 0.6239 | 0.6556 | 0.7826 | 0.7135 |
| 0.0074 | 15.0 | 3675 | 0.5931 | 0.6825 | 0.7599 | 0.7191 |
| 0.0074 | 16.0 | 3920 | 0.6364 | 0.6785 | 0.7580 | 0.7161 |
| 0.005 | 17.0 | 4165 | 0.6437 | 0.6855 | 0.7580 | 0.7199 |
| 0.005 | 18.0 | 4410 | 0.6610 | 0.6779 | 0.7599 | 0.7166 |
| 0.0029 | 19.0 | 4655 | 0.6625 | 0.6853 | 0.7656 | 0.7232 |
| 0.0029 | 20.0 | 4900 | 0.6645 | 0.6885 | 0.7665 | 0.7254 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-ner-kmeans-twitter", "results": []}]} | token-classification | ArBert/roberta-base-finetuned-ner-kmeans-twitter | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner-kmeans-twitter
=========================================
This model is a fine-tuned version of ArBert/roberta-base-finetuned-ner on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6645
* Precision: 0.6885
* Recall: 0.7665
* F1: 0.7254
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: 20
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner-kmeans
This model is a fine-tuned version of [ArBert/roberta-base-finetuned-ner](https://huggingface.co/ArBert/roberta-base-finetuned-ner) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- Precision: 0.9559
- Recall: 0.9615
- F1: 0.9587
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.0248 | 1.0 | 878 | 0.0609 | 0.9507 | 0.9561 | 0.9534 |
| 0.0163 | 2.0 | 1756 | 0.0640 | 0.9515 | 0.9578 | 0.9546 |
| 0.0089 | 3.0 | 2634 | 0.0592 | 0.9559 | 0.9615 | 0.9587 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "roberta-base-finetuned-ner-kmeans", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.955868544600939, "name": "Precision"}, {"type": "recall", "value": 0.9614658103513412, "name": "Recall"}, {"type": "f1", "value": 0.9586590074394953, "name": "F1"}]}]}]} | token-classification | ArBert/roberta-base-finetuned-ner-kmeans | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner-kmeans
=================================
This model is a fine-tuned version of ArBert/roberta-base-finetuned-ner on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0592
* Precision: 0.9559
* Recall: 0.9615
* F1: 0.9587
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0738
- Precision: 0.9232
- Recall: 0.9437
- F1: 0.9333
- Accuracy: 0.9825
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1397 | 1.0 | 1368 | 0.0957 | 0.9141 | 0.9048 | 0.9094 | 0.9753 |
| 0.0793 | 2.0 | 2736 | 0.0728 | 0.9274 | 0.9324 | 0.9299 | 0.9811 |
| 0.0499 | 3.0 | 4104 | 0.0738 | 0.9232 | 0.9437 | 0.9333 | 0.9825 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-ner", "results": []}]} | token-classification | ArBert/roberta-base-finetuned-ner | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0738
* Precision: 0.9232
* Recall: 0.9437
* F1: 0.9333
* Accuracy: 0.9825
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
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null | null | transformers |
# Stark DialoGPT Model | {"tags": ["conversational"]} | text-generation | ArJakusz/DialoGPT-small-stark | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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# Stark DialoGPT Model | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Aran/DialoGPT-medium-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Aran/DialoGPT-small-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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null | null | transformers | # Rick DialoGPT Model
| {"tags": ["conversational"]} | text-generation | Arcktosh/DialoGPT-small-rick | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Rick DialoGPT Model
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null | null | transformers | # Cultured Kumiko DialoGPT Model | {"tags": ["conversational"]} | text-generation | AriakimTaiyo/DialoGPT-cultured-Kumiko | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Cultured Kumiko DialoGPT Model | [
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null | null | null | # Medium Kumiko DialoGPT Model | {"tags": ["conversational"]} | text-generation | AriakimTaiyo/DialoGPT-medium-Kumiko | [
"conversational",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
| # Medium Kumiko DialoGPT Model | [
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null | null | transformers | # Revised Kumiko DialoGPT Model | {"tags": ["conversational"]} | text-generation | AriakimTaiyo/DialoGPT-revised-Kumiko | [
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null | null | transformers | # Kumiko DialoGPT Model | {"tags": ["conversational"]} | text-generation | AriakimTaiyo/DialoGPT-small-Kumiko | [
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"conversational",
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null | null | transformers | # Rikka DialoGPT Model | {"tags": ["conversational"]} | text-generation | AriakimTaiyo/DialoGPT-small-Rikka | [
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"text-generation",
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hausa2-demo-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: 1.2032
- Wer: 0.7237
## 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
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1683 | 12.49 | 400 | 1.0279 | 0.7211 |
| 0.0995 | 24.98 | 800 | 1.2032 | 0.7237 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-hausa2-demo-colab", "results": []}]} | automatic-speech-recognition | Arnold/wav2vec2-hausa2-demo-colab | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-hausa2-demo-colab
==========================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2032
* Wer: 0.7237
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 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
| [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-hausa2-demo-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: 0.2993
- Wer: 0.4826
## 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: 9.6e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 13
- gradient_accumulation_steps: 3
- total_train_batch_size: 36
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.1549 | 12.5 | 400 | 2.7289 | 1.0 |
| 2.0566 | 25.0 | 800 | 0.4582 | 0.6768 |
| 0.4423 | 37.5 | 1200 | 0.3037 | 0.5138 |
| 0.2991 | 50.0 | 1600 | 0.2993 | 0.4826 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hausa2-demo-colab", "results": []}]} | automatic-speech-recognition | Arnold/wav2vec2-large-xlsr-hausa2-demo-colab | [
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-hausa2-demo-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2993
* Wer: 0.4826
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: 9.6e-05
* train\_batch\_size: 12
* eval\_batch\_size: 8
* seed: 13
* gradient\_accumulation\_steps: 3
* total\_train\_batch\_size: 36
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 400
* num\_epochs: 50
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2295
- Accuracy: 0.92
- F1: 0.9202
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8187 | 1.0 | 250 | 0.3137 | 0.902 | 0.8983 |
| 0.2514 | 2.0 | 500 | 0.2295 | 0.92 | 0.9202 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.92, "name": "Accuracy"}, {"type": "f1", "value": 0.9201604193183255, "name": "F1"}]}]}]} | text-classification | Aron/distilbert-base-uncased-finetuned-emotion | [
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2295
* Accuracy: 0.92
* F1: 0.9202
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
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null | null | transformers |
#Okarin Bot | {"tags": ["conversational"]} | text-generation | ArtemisZealot/DialoGTP-small-Qkarin | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Aruden/DialoGPT-medium-harrypotterall | [
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0.11647630482912064,
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0.024608... |
null | null | transformers |
```
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("ArvinZhuang/BiTAG-t5-large")
tokenizer = AutoTokenizer.from_pretrained("ArvinZhuang/BiTAG-t5-large")
text = "abstract: [your abstract]" # use 'title:' as the prefix for title_to_abs task.
input_ids = tokenizer.encode(text, return_tensors='pt')
outputs = model.generate(
input_ids,
do_sample=True,
max_length=500,
top_p=0.9,
top_k=20,
temperature=1,
num_return_sequences=10,
)
print("Output:\n" + 100 * '-')
for i, output in enumerate(outputs):
print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True)))
```
GitHub: https://github.com/ArvinZhuang/BiTAG | {"inference": {"parameters": {"do_sample": true, "max_length": 500, "top_p": 0.9, "top_k": 20, "temperature": 1, "num_return_sequences": 10}}, "widget": [{"text": "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).", "example_title": "BERT abstract"}]} | text2text-generation | ielabgroup/BiTAG-t5-large | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
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null | null | transformers |
# Model Trained Using AutoNLP
- Model: Google's Pegasus (https://huggingface.co/google/pegasus-xsum)
- Problem type: Summarization
- Model ID: 34558227
- CO2 Emissions (in grams): 137.60574081887984
- Spaces: https://huggingface.co/spaces/TitleGenerators/ArxivTitleGenerator
- Dataset: arXiv Dataset (https://www.kaggle.com/Cornell-University/arxiv)
- Data subset used: https://huggingface.co/datasets/AryanLala/autonlp-data-Scientific_Title_Generator
## Validation Metrics
- Loss: 2.578599214553833
- Rouge1: 44.8482
- Rouge2: 24.4052
- RougeL: 40.1716
- RougeLsum: 40.1396
- Gen Len: 11.4675
## Social
- LinkedIn: https://www.linkedin.com/in/aryanlala/
- Twitter: https://twitter.com/AryanLala20
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/AryanLala/autonlp-Scientific_Title_Generator-34558227
``` | {"language": "en", "tags": "autonlp", "datasets": ["AryanLala/autonlp-data-Scientific_Title_Generator"], "widget": [{"text": "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets."}], "co2_eq_emissions": 137.60574081887984} | text2text-generation | AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Model: Google's Pegasus (URL
- Problem type: Summarization
- Model ID: 34558227
- CO2 Emissions (in grams): 137.60574081887984
- Spaces: URL
- Dataset: arXiv Dataset (URL
- Data subset used: URL
## Validation Metrics
- Loss: 2.578599214553833
- Rouge1: 44.8482
- Rouge2: 24.4052
- RougeL: 40.1716
- RougeLsum: 40.1396
- Gen Len: 11.4675
## Social
- LinkedIn: URL
- Twitter: URL
## Usage
You can use cURL to access this model:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-parsbert-uncased-finetuned
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2045
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5596 | 1.0 | 515 | 3.2097 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"]} | fill-mask | Ashkanmh/bert-base-parsbert-uncased-finetuned | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-parsbert-uncased-finetuned
====================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2045
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.10.0
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
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null | null | transformers |
A discord chatbot trained on the whole LiS script to simulate character speech | {"tags": ["conversational"]} | text-generation | Aspect11/DialoGPT-Medium-LiSBot | [
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null | null | transformers | # RinTohsaka bot | {"tags": ["conversational"]} | text-generation | Asuramaru/DialoGPT-small-rintohsaka | [
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null | null | transformers | GPT-Glacier, a GPT-Neo 125M model finetuned on the Glacier2 Modding Discord server. | {} | text-generation | Atampy26/GPT-Glacier | [
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null | null | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | text-generation | Atchuth/DialoGPT-small-MichaelBot | [
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"conversational",
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | null | Placeholder | {} | null | Atlasky/Turkish-Negator | [
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null | null | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | text-generation | Augustvember/WOKKAWOKKA | [
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null | null | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | text-generation | Augustvember/test | [
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null | null | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | text-generation | Augustvember/wokka5 | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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null | null | transformers | #MyAwesomeModel | {"tags": ["conversational"]} | text-generation | Augustvember/wokkabottest2 | [
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