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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers | A simple question-generation model built based on SQuAD 2.0 dataset.
Example use:
```python
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
model_name = "allenai/t5-small-squad2-question-generation"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.fro... | {"language": "en"} | allenai/t5-small-squad2-question-generation | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| A simple question-generation model built based on SQuAD 2.0 dataset.
Example use:
which should result in the following:
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Tailor
## Model description
This is a ported version of [Tailor](https://homes.cs.washington.edu/~wtshuang/static/papers/2021-arxiv-tailor.pdf), the general-purpose counterfactual generator.
For more code release, please refer to [this github page](https://github.com/allenai/tailor).
#### How to use
```python
f... | {"language": "en", "tags": ["controlled generation", "perturbation"], "widget": [{"text": "[VERB+passive+past: break | PATIENT+partial: cup] <extra_id_0> <extra_id_1> <extra_id_2> ."}, {}]} | allenai/tailor | null | [
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"region:us"
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"2107.07150"
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|
# Tailor
## Model description
This is a ported version of Tailor, the general-purpose counterfactual generator.
For more code release, please refer to this github page.
#### How to use
### BibTeX entry and citation info
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question-answering | allennlp |
A reading comprehension model patterned after the proposed model in Devlin et al, with improvements borrowed from the SQuAD model in the transformers project
The model implements a reading comprehension model patterned after the proposed model in BERT: Pre-training of Deep Bidirectional Transformers for Language Under... | {"language": "en", "tags": ["allennlp", "question-answering"]} | allenai/transformer_qa | null | [
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|
A reading comprehension model patterned after the proposed model in Devlin et al, with improvements borrowed from the SQuAD model in the transformers project
The model implements a reading comprehension model patterned after the proposed model in BERT: Pre-training of Deep Bidirectional Transformers for Language Under... | [] | [
"TAGS\n#allennlp #tensorboard #question-answering #en #region-us \n"
] |
text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa
| {"language": "en"} | allenai/unifiedqa-v2-t5-11b-1251000 | null | [
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"en"
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#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa | {"language": "en"} | allenai/unifiedqa-v2-t5-11b-1363200 | null | [
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"# Further details: URL"
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa
| {"language": "en"} | allenai/unifiedqa-v2-t5-3b-1251000 | null | [
"transformers",
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"autotrain_compatible",
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"en"
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#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa
| {"language": "en"} | allenai/unifiedqa-v2-t5-3b-1363200 | null | [
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"en"
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#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa | {"language": "en"} | allenai/unifiedqa-v2-t5-base-1251000 | null | [
"transformers",
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"t5",
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"en",
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa
| {"language": "en"} | allenai/unifiedqa-v2-t5-base-1363200 | null | [
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"autotrain_compatible",
"endpoints_compatible",
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"region:us"
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#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa
| {"language": "en"} | allenai/unifiedqa-v2-t5-large-1251000 | null | [
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa
| {"language": "en"} | allenai/unifiedqa-v2-t5-large-1363200 | null | [
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"en"
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#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa | {"language": "en"} | allenai/unifiedqa-v2-t5-small-1251000 | null | [
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text2text-generation | transformers | # Further details: https://github.com/allenai/unifiedqa | {"language": "en"} | allenai/unifiedqa-v2-t5-small-1363200 | null | [
"transformers",
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"en",
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"has_space",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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#transformers #pytorch #t5 #text2text-generation #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
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] |
translation | transformers |
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 mo... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation", "wmt16", "allenai"], "datasets": ["wmt16"], "metrics": ["bleu"]} | allenai/wmt16-en-de-12-1 | null | [
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq-based wmt16 transformer for en-de.
For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.
All 3 models are available:
* wmt16-en-de-dist-12-1
* wmt16-en-de-di... | [
"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.\n\n\nEval results\n------------\n\n\nHere are the BLEU scores:\n\n\nmodel: wmt16-en-de-12-1, fairseq: 26.9... | [
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"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights ... |
translation | transformers |
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 mo... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation", "wmt16", "allenai"], "datasets": ["wmt16"], "metrics": ["bleu"]} | allenai/wmt16-en-de-dist-12-1 | null | [
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq-based wmt16 transformer for en-de.
For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.
All 3 models are available:
* wmt16-en-de-dist-12-1
* wmt16-en-de-di... | [
"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.\n\n\nEval results\n------------\n\n\nHere are the BLEU scores:\n\n\nmodel: wmt16-en-de-dist-12-1, fairseq:... | [
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"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights ... |
translation | transformers |
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 mo... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation", "wmt16", "allenai"], "datasets": ["wmt16"], "metrics": ["bleu"]} | allenai/wmt16-en-de-dist-6-1 | null | [
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq-based wmt16 transformer for en-de.
For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.
All 3 models are available:
* wmt16-en-de-dist-12-1
* wmt16-en-de-di... | [
"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.\n\n\nEval results\n------------\n\n\nHere are the BLEU scores:\n\n\nmodel: wmt16-en-de-dist-6-1, fairseq: ... | [
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"#### How to use",
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translation | transformers |
# FSMT
## Model description
This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
2 models... | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["translation", "wmt19", "allenai"], "datasets": ["wmt19"], "metrics": ["bleu"]} | allenai/wmt19-de-en-6-6-base | null | [
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"license:apache-2.0",
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"2006.10369"
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"de",
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq-based wmt19 transformer for de-en.
For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.
2 models are available:
* wmt19-de-en-6-6-big
* wmt19-de-en-6-6-base... | [
"#### How to use",
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"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights ... |
translation | transformers |
# FSMT
## Model description
This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
2 models... | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["translation", "wmt19", "allenai"], "datasets": ["wmt19"], "metrics": ["bleu"]} | allenai/wmt19-de-en-6-6-big | null | [
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"2006.10369"
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"de",
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#transformers #pytorch #fsmt #text2text-generation #translation #wmt19 #allenai #de #en #dataset-wmt19 #arxiv-2006.10369 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FSMT
====
Model description
-----------------
This is a ported version of fairseq-based wmt19 transformer for de-en.
For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.
2 models are available:
* wmt19-de-en-6-6-big
* wmt19-de-en-6-6-base... | [
"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.\n\n\nEval results\n------------\n\n\nHere are the BLEU scores:\n\n\n\nThe score was calculated using this ... | [
"TAGS\n#transformers #pytorch #fsmt #text2text-generation #translation #wmt19 #allenai #de #en #dataset-wmt19 #arxiv-2006.10369 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use",
"#### Limitations and bias\n\n\nTraining data\n-------------\n\n\nPretrained weights ... |
null | transformers |
# Model name
Chinese-bert-wwm-electrical-health-records-ner-question-answering-sequence-labeling
#### How to use
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("allenyummy/chinese-bert-wwm-ehr-ner-qasl")
model = AutoModelForTokenClassificati... | {"language": "zh-tw"} | allenyummy/chinese-bert-wwm-ehr-ner-qasl | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-tw"
] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
|
# Model name
Chinese-bert-wwm-electrical-health-records-ner-question-answering-sequence-labeling
#### How to use
| [
"# Model name\nChinese-bert-wwm-electrical-health-records-ner-question-answering-sequence-labeling",
"#### How to use"
] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# Model name\nChinese-bert-wwm-electrical-health-records-ner-question-answering-sequence-labeling",
"#### How to use"
] |
null | transformers |
# Model name
Chinese-bert-wwm-electrical-health-records-ner-sequence-labeling
#### How to use
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("allenyummy/chinese-bert-wwm-ehr-ner-sl")
model = AutoModelForTokenClassification.from_pretrained("a... | {"language": "zh-tw"} | allenyummy/chinese-bert-wwm-ehr-ner-sl | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-tw"
] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
|
# Model name
Chinese-bert-wwm-electrical-health-records-ner-sequence-labeling
#### How to use
| [
"# Model name\nChinese-bert-wwm-electrical-health-records-ner-sequence-labeling",
"#### How to use"
] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# Model name\nChinese-bert-wwm-electrical-health-records-ner-sequence-labeling",
"#### How to use"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Swahili
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swahili using the following datasets:
- [ALFFA](http://www.openslr.org/25/),
- [Gamayun](https://gamayun.translatorswb.org/download/gamayun-5k-english-swahili/)
- [IWSLT](https://... | {"language": "sw", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["ALFFA,Gamayun & IWSLT"], "metrics": ["wer"]} | alokmatta/wav2vec2-large-xlsr-53-sw | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sw",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sw #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Swahili
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:
- ALFFA,
- Gamayun
- IWSLT
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:
Test Resul... | [
"# Wav2Vec2-Large-XLSR-53-Swahili \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:\n- ALFFA,\n- Gamayun \n- IWSLT\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sw #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Swahili \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:\n- ALFFA,\n- Gama... |
question-answering | transformers |
# bert-base-multilingual-uncased for multilingual QA
# Overview
**Language Model**: bert-base-multilingual-uncased \
**Downstream task**: Extractive QA \
**Training data**: [XQuAD](https://github.com/deepmind/xquad) \
**Testing Data**: [XQuAD](https://github.com/deepmind/xquad)
# Hyperparameters
```python
batch_siz... | {"tags": ["multilingual"], "datasets": ["xquad"]} | alon-albalak/bert-base-multilingual-xquad | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"multilingual",
"dataset:xquad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #multilingual #dataset-xquad #endpoints_compatible #region-us
|
# bert-base-multilingual-uncased for multilingual QA
# Overview
Language Model: bert-base-multilingual-uncased \
Downstream task: Extractive QA \
Training data: XQuAD \
Testing Data: XQuAD
# Hyperparameters
# Performance
Evaluated on held-out test set from XQuAD
# Usage
## In Transformers
## In FARM
## In... | [
"# bert-base-multilingual-uncased for multilingual QA",
"# Overview\nLanguage Model: bert-base-multilingual-uncased \\\nDownstream task: Extractive QA \\\nTraining data: XQuAD \\\nTesting Data: XQuAD",
"# Hyperparameters",
"# Performance\n\nEvaluated on held-out test set from XQuAD",
"# Usage",
"## In Tra... | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #multilingual #dataset-xquad #endpoints_compatible #region-us \n",
"# bert-base-multilingual-uncased for multilingual QA",
"# Overview\nLanguage Model: bert-base-multilingual-uncased \\\nDownstream task: Extractive QA \\\nTraining data: XQuAD ... |
question-answering | transformers | # xlm-roberta-base for multilingual QA
# Overview
**Language Model**: xlm-roberta-base \
**Downstream task**: Extractive QA \
**Training data**: [XQuAD](https://github.com/deepmind/xquad)\
**Testing Data**: [XQuAD](https://github.com/deepmind/xquad)
# Hyperparameters
```python
batch_size = 40
n_epochs = 10
max_seq_len... | {"tags": ["multilingual"], "datasets": ["xquad"]} | alon-albalak/xlm-roberta-base-xquad | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"multilingual",
"dataset:xquad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #multilingual #dataset-xquad #endpoints_compatible #region-us
| # xlm-roberta-base for multilingual QA
# Overview
Language Model: xlm-roberta-base \
Downstream task: Extractive QA \
Training data: XQuAD\
Testing Data: XQuAD
# Hyperparameters
# Performance
Evaluated on held-out test set from XQuAD
# Usage
## In Transformers
## In FARM
## In Haystack
Usage instructions fo... | [
"# xlm-roberta-base for multilingual QA",
"# Overview\nLanguage Model: xlm-roberta-base \\\nDownstream task: Extractive QA \\\nTraining data: XQuAD\\\nTesting Data: XQuAD",
"# Hyperparameters",
"# Performance\nEvaluated on held-out test set from XQuAD",
"# Usage",
"## In Transformers",
"## In FARM",
"... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #multilingual #dataset-xquad #endpoints_compatible #region-us \n",
"# xlm-roberta-base for multilingual QA",
"# Overview\nLanguage Model: xlm-roberta-base \\\nDownstream task: Extractive QA \\\nTraining data: XQuAD\\\nTesting Data: XQuAD",
"# Hype... |
question-answering | transformers |
# xlm-roberta-large for multilingual QA
# Overview
**Language Model**: xlm-roberta-large \
**Downstream task**: Extractive QA \
**Training data**: [XQuAD](https://github.com/deepmind/xquad) \
**Testing Data**: [XQuAD](https://github.com/deepmind/xquad)
# Hyperparameters
```python
batch_size = 48
n_epochs = 13
max_s... | {"tags": ["multilingual"], "datasets": ["xquad"]} | alon-albalak/xlm-roberta-large-xquad | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"question-answering",
"multilingual",
"dataset:xquad",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-xquad #endpoints_compatible #has_space #region-us
|
# xlm-roberta-large for multilingual QA
# Overview
Language Model: xlm-roberta-large \
Downstream task: Extractive QA \
Training data: XQuAD \
Testing Data: XQuAD
# Hyperparameters
# Performance
Evaluated on held-out test set from XQuAD
# Usage
## In Transformers
## In FARM
## In Haystack
Usage instruc... | [
"# xlm-roberta-large for multilingual QA",
"# Overview\nLanguage Model: xlm-roberta-large \\\nDownstream task: Extractive QA \\\nTraining data: XQuAD \\\nTesting Data: XQuAD",
"# Hyperparameters",
"# Performance\n\nEvaluated on held-out test set from XQuAD",
"# Usage",
"## In Transformers",
"## In FARM"... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #question-answering #multilingual #dataset-xquad #endpoints_compatible #has_space #region-us \n",
"# xlm-roberta-large for multilingual QA",
"# Overview\nLanguage Model: xlm-roberta-large \\\nDownstream task: Extractive QA \\\nTraining data: XQuAD \\\nTest... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171
## Validation Metrics
- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.886464676654089... | {"language": "en", "tags": "autonlp", "datasets": ["alperiox/autonlp-data-user-review-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.268309634217171} | alperiox/autonlp-user-review-classification-536415182 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:alperiox/autonlp-data-user-review-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-alperiox/autonlp-data-user-review-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171
## Validation Metrics
- Loss: 0.44733062386512756
- Accuracy: 0.8873239436619719
- Macro F1: 0.8859416445623343
- Micro F1: 0.8873239436619719
- Weighted F1: 0.886464676654089... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 536415182\n- CO2 Emissions (in grams): 1.268309634217171",
"## Validation Metrics\n\n- Loss: 0.44733062386512756\n- Accuracy: 0.8873239436619719\n- Macro F1: 0.8859416445623343\n- Micro F1: 0.8873239436619719\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-alperiox/autonlp-data-user-review-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 536415182\n- CO2 E... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Auth... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | alphai/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (1 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1 labels for 1 components)",
"### Accuracy"
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | aluserhuggingface/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
BioBERT model fine-tuned in NER task with BC5CDR-chemicals and BC4CHEMD corpus.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner | {"language": "en", "license": "apache-2.0", "tags": ["token-classification", "NER", "Biomedical", "Chemicals"], "datasets": ["BC5CDR-chemicals", "BC4CHEMD"]} | alvaroalon2/biobert_chemical_ner | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"NER",
"Biomedical",
"Chemicals",
"en",
"dataset:BC5CDR-chemicals",
"dataset:BC4CHEMD",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #NER #Biomedical #Chemicals #en #dataset-BC5CDR-chemicals #dataset-BC4CHEMD #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
BioBERT model fine-tuned in NER task with BC5CDR-chemicals and BC4CHEMD corpus.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: URL | [] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #NER #Biomedical #Chemicals #en #dataset-BC5CDR-chemicals #dataset-BC4CHEMD #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers | BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner | {"language": "en", "license": "apache-2.0", "tags": ["token-classification", "NER", "Biomedical", "Diseases"], "datasets": ["BC5CDR-diseases", "ncbi_disease"]} | alvaroalon2/biobert_diseases_ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"Biomedical",
"Diseases",
"en",
"dataset:BC5CDR-diseases",
"dataset:ncbi_disease",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #Biomedical #Diseases #en #dataset-BC5CDR-diseases #dataset-ncbi_disease #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #Biomedical #Diseases #en #dataset-BC5CDR-diseases #dataset-ncbi_disease #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers | BioBERT model fine-tuned in NER task with JNLPBA and BC2GM corpus for genetic class entities.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner | {"language": "en", "license": "apache-2.0", "tags": ["token-classification", "NER", "Biomedical", "Genetics"], "datasets": ["JNLPBA", "BC2GM"]} | alvaroalon2/biobert_genetic_ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"Biomedical",
"Genetics",
"en",
"dataset:JNLPBA",
"dataset:BC2GM",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #NER #Biomedical #Genetics #en #dataset-JNLPBA #dataset-BC2GM #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| BioBERT model fine-tuned in NER task with JNLPBA and BC2GM corpus for genetic class entities.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #NER #Biomedical #Genetics #en #dataset-JNLPBA #dataset-BC2GM #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null | Hi! | {} | alvinhou/model_test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Hi! | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Frank Talks DialoGPT Model | {"tags": ["conversational"]} | alvinkobe/DialoGPT-medium-steve_biko | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Frank Talks DialoGPT Model | [
"# Frank Talks DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Frank Talks DialoGPT Model"
] |
text-generation | transformers |
#PANAFRICAN DialoGPT | {"tags": ["conversational"]} | alvinkobe/DialoGPT-small-KST | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#PANAFRICAN DialoGPT | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 34318169
- CO2 Emissions (in grams): 8.612473981829835
## Validation Metrics
- Loss: 1.3520570993423462
- Accuracy: 0.6083916083916084
- Macro F1: 0.5420169617715481
- Micro F1: 0.6083916083916084
- Weighted F1: 0.5963328136975058
... | {"language": "unk", "tags": "autonlp", "datasets": ["alvp/autonlp-data-alberti-stanza-names"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 8.612473981829835} | alvp/alberti-stanzas | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:alvp/autonlp-data-alberti-stanza-names",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-alvp/autonlp-data-alberti-stanza-names #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 34318169
- CO2 Emissions (in grams): 8.612473981829835
## Validation Metrics
- Loss: 1.3520570993423462
- Accuracy: 0.6083916083916084
- Macro F1: 0.5420169617715481
- Micro F1: 0.6083916083916084
- Weighted F1: 0.5963328136975058
... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 34318169\n- CO2 Emissions (in grams): 8.612473981829835",
"## Validation Metrics\n\n- Loss: 1.3520570993423462\n- Accuracy: 0.6083916083916084\n- Macro F1: 0.5420169617715481\n- Micro F1: 0.6083916083916084\n- Weighted F1: 0... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-alvp/autonlp-data-alberti-stanza-names #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 34318169\n- CO2 Emissions (... |
fill-mask | 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. -->
# 57426955
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-case... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "57426955", "results": []}]} | am-shb/bert-base-multilingual-cased-finetuned | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# 57426955
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More in... | [
"# 57426955\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4779",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ev... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# 57426955\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4779",
"#... |
fill-mask | 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. -->
# 57463134
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-un... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "57463134", "results": []}]} | am-shb/bert-base-multilingual-uncased-finetuned | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# 57463134
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6137
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More ... | [
"# 57463134\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6137",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# 57463134\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6137",
... |
fill-mask | 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-multilingual-uncased
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-multilingual-uncased", "results": []}]} | am-shb/bert-base-multilingual-uncased-pretrained | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-multilingual-uncased
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2198
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and ... | [
"# bert-base-multilingual-uncased\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2198",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-multilingual-uncased\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results on the evaluation set... |
fill-mask | 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
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
I... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta", "results": []}]} | am-shb/xlm-roberta-base-pretrained | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# roberta
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4144
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information nee... | [
"# roberta\n\nThis model is a fine-tuned version of xlm-roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4144",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta\n\nThis model is a fine-tuned version of xlm-roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4144",
"## Mode... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445
## Validation Metrics
- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.65327... | {"language": "en", "tags": "autonlp", "datasets": ["am4nsolanki/autonlp-data-text-hateful-memes"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.4280361775467445} | am4nsolanki/autonlp-text-hateful-memes-36789092 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
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"dataset:am4nsolanki/autonlp-data-text-hateful-memes",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-am4nsolanki/autonlp-data-text-hateful-memes #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36789092
- CO2 Emissions (in grams): 1.4280361775467445
## Validation Metrics
- Loss: 0.5255328416824341
- Accuracy: 0.7666078777189889
- Precision: 0.6913123844731978
- Recall: 0.6192052980132451
- AUC: 0.7893359070795125
- F1: 0.65327... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 36789092\n- CO2 Emissions (in grams): 1.4280361775467445",
"## Validation Metrics\n\n- Loss: 0.5255328416824341\n- Accuracy: 0.7666078777189889\n- Precision: 0.6913123844731978\n- Recall: 0.6192052980132451\n- AUC: 0.789335907079... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-am4nsolanki/autonlp-data-text-hateful-memes #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 36789092\n- CO2 Emissi... |
fill-mask | transformers |
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison demo is available here](https://huggingface.co/spaces/flax-community/roberta-hindi).
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingfac... | {"widget": [{"text": "\u092e\u0941\u091d\u0947 \u0909\u0928\u0938\u0947 \u092c\u093e\u0924 \u0915\u0930\u0928\u093e <mask> \u0905\u091a\u094d\u091b\u093e \u0932\u0917\u093e"}, {"text": "\u0939\u092e \u0906\u092a\u0915\u0947 \u0938\u0941\u0916\u0926 <mask> \u0915\u0940 \u0915\u093e\u092e\u0928\u093e \u0915\u0930\u0924\u... | amankhandelia/panini | null | [
"transformers",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa base model for Hindi language
=====================================
Pretrained model on Hindi language using a masked language modeling (MLM) objective. A more interactive & comparison demo is available here.
>
> This is part of the
> Flax/Jax Community Week, organized by Hugging Face and TPU usage sponso... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining data\n-------------\n\n\nThe RoBERTa Hindi model was pretrained on the reunion of the following datasets:\n\n\n* OSCAR is a huge multilingual corpus obtained by language classification and filtering of t... | [
"TAGS\n#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining data\n-------------\n\n\nThe RoBERTa Hindi model was pretrained on the reunion of the following data... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 20114061
- CO2 Emissions (in grams): 3.651199395353127
## Validation Metrics
- Loss: 0.5046541690826416
- Accuracy: 0.8036219581211093
- Macro F1: 0.807095210403678
- Micro F1: 0.8036219581211093
- Weighted F1: 0.8039634739225368
-... | {"language": "en", "tags": "autonlp", "datasets": ["amansolanki/autonlp-data-Tweet-Sentiment-Extraction"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.651199395353127} | amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:amansolanki/autonlp-data-Tweet-Sentiment-Extraction",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-amansolanki/autonlp-data-Tweet-Sentiment-Extraction #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 20114061
- CO2 Emissions (in grams): 3.651199395353127
## Validation Metrics
- Loss: 0.5046541690826416
- Accuracy: 0.8036219581211093
- Macro F1: 0.807095210403678
- Micro F1: 0.8036219581211093
- Weighted F1: 0.8039634739225368
-... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 20114061\n- CO2 Emissions (in grams): 3.651199395353127",
"## Validation Metrics\n\n- Loss: 0.5046541690826416\n- Accuracy: 0.8036219581211093\n- Macro F1: 0.807095210403678\n- Micro F1: 0.8036219581211093\n- Weighted F1: 0.... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-amansolanki/autonlp-data-Tweet-Sentiment-Extraction #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 20114061\... |
fill-mask | transformers | ⚠️ **Disclaimer** ⚠️
This model is community-contributed, and not supported by Amazon, Inc.
## BORT
[Amazon's BORT](https://www.amazon.science/blog/a-version-of-the-bert-language-model-thats-20-times-as-fast)
BORT is a highly compressed version of [bert-large](https://huggingface.co/bert-large-uncased) that is up ... | {} | amazon/bort | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"arxiv:2010.10499",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.10499"
] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #arxiv-2010.10499 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ️ Disclaimer ️
This model is community-contributed, and not supported by Amazon, Inc.
## BORT
Amazon's BORT
BORT is a highly compressed version of bert-large that is up to 10 times faster at inference.
The model is an optimal sub-architecture of *bert-large* that was found using neural architecture search.
Paper... | [
"## BORT\n\nAmazon's BORT\n\nBORT is a highly compressed version of bert-large that is up to 10 times faster at inference. \nThe model is an optimal sub-architecture of *bert-large* that was found using neural architecture search.\n\nPaper\n\nAbstract\n\nWe extract an optimal subset of architectural parameters for ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #arxiv-2010.10499 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## BORT\n\nAmazon's BORT\n\nBORT is a highly compressed version of bert-large that is up to 10 times faster at inference. \nThe model is an optimal sub-architecture of ... |
text2text-generation | 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. -->
# encoder_decoder_es
This model is a fine-tuned version of [](https://huggingface.co/) on the cc_news_es_titles dataset.
It achiev... | {"tags": ["generated_from_trainer"], "datasets": ["cc_news_es_titles"], "model-index": [{"name": "encoder_decoder_es", "results": []}]} | amazon-sagemaker-community/encoder_decoder_es | null | [
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"text2text-generation",
"generated_from_trainer",
"dataset:cc_news_es_titles",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cc_news_es_titles #autotrain_compatible #endpoints_compatible #has_space #region-us
| encoder\_decoder\_es
====================
This model is a fine-tuned version of [](URL on the cc\_news\_es\_titles dataset.
It achieves the following results on the evaluation set:
* Loss: 7.8773
* Rouge2 Precision: 0.002
* Rouge2 Recall: 0.0116
* Rouge2 Fmeasure: 0.0034
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #dataset-cc_news_es_titles #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-en-ru-emoji-v2
This model is a fine-tuned version of [DeepPavlov/xlm-roberta-large-en-ru](https://huggingface.co/Dee... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "DeepPavlov/xlm-roberta-large-en-ru", "model-index": [{"name": "xlm-roberta-en-ru-emoji-v2", "results": []}]} | amazon-sagemaker-community/xlm-roberta-en-ru-emoji-v2 | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
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"base_model:DeepPavlov/xlm-roberta-large-en-ru",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-DeepPavlov/xlm-roberta-large-en-ru #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-en-ru-emoji-v2
==========================
This model is a fine-tuned version of DeepPavlov/xlm-roberta-large-en-ru on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3356
* Accuracy: 0.3102
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\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 #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-DeepPavlov/xlm-roberta-large-en-ru #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
text-classification | transformers |
# Passage Reranking Multilingual BERT 🔃 🌍
## Model description
**Input:** Supports over 100 Languages. See [List of supported languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) for all available.
**Purpose:** This module takes a search query [1] and a passage [2] an... | {"language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "he", "hi", "hu", "is", "io", "id", "ga", "it", "ja", "jv", "kn", "kk"... | amberoad/bert-multilingual-passage-reranking-msmarco | null | [
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"bg",
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"ca",
"ceb",
"ce",
"zh",
"cv",
... | null | 2022-03-02T23:29:05+00:00 | [
"1901.04085"
] | [
"multilingual",
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"hy",
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"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
... | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #msmarco #multilingual #passage reranking #af #sq #ar #an #hy #ast #az #ba #eu #bar #be #bn #inc #bs #br #bg #my #ca #ceb #ce #zh #cv #hr #cs #da #nl #en #et #fi #fr #gl #ka #de #el #gu #ht #he #hi #hu #is #io #id #ga #it #ja #jv #kn #kk #ky #ko #la #lv #l... | Passage Reranking Multilingual BERT
===================================
Model description
-----------------
Input: Supports over 100 Languages. See List of supported languages for all available.
Purpose: This module takes a search query [1] and a passage [2] and calculates if the passage matches the query.
It can... | [
"#### How to use\n\n\nThis Model can be used as a drop-in replacement in the Nboost Library\nThrough this you can directly improve your Elasticsearch Results without any coding.\n\n\nTraining data\n-------------\n\n\nThis model is trained using the Microsoft MS Marco Dataset. This training dataset contains approxim... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #msmarco #multilingual #passage reranking #af #sq #ar #an #hy #ast #az #ba #eu #bar #be #bn #inc #bs #br #bg #my #ca #ceb #ce #zh #cv #hr #cs #da #nl #en #et #fi #fr #gl #ka #de #el #gu #ht #he #hi #hu #is #io #id #ga #it #ja #jv #kn #kk #ky #ko #la ... |
fill-mask | transformers |
# bert-base-5lang-cased
This is a smaller version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handles only 5 languages (en, fr, es, de and zh) instead of 104.
The model is therefore 30% smaller than the original one (124M parameters instead of 178M) but gives exactly the... | {"language": ["en", "fr", "es", "de", "zh", "multilingual"], "license": "apache-2.0", "tags": ["pytorch", "bert", "multilingual", "en", "fr", "es", "de", "zh"], "datasets": "wikipedia", "inference": false} | amine/bert-base-5lang-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"multilingual",
"en",
"fr",
"es",
"de",
"zh",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"fr",
"es",
"de",
"zh",
"multilingual"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #en #fr #es #de #zh #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #region-us
| bert-base-5lang-cased
=====================
This is a smaller version of bert-base-multilingual-cased that handles only 5 languages (en, fr, es, de and zh) instead of 104.
The model is therefore 30% smaller than the original one (124M parameters instead of 178M) but gives exactly the same representations for the abov... | [
"### How to cite\n\n\nContact\n-------\n\n\nPlease contact amine@URL for any question, feedback or request."
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #en #fr #es #de #zh #dataset-wikipedia #license-apache-2.0 #autotrain_compatible #region-us \n",
"### How to cite\n\n\nContact\n-------\n\n\nPlease contact amine@URL for any question, feedback or request."
] |
text-classification | 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. -->
# pft-clf-finetuned
This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/ber... | {"language": "fa", "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "widget": [{"text": "\u0627\u0645\u0631\u0648\u0632 \u062f\u0631\u0628\u06cc \u062f\u0648 \u062a\u06cc\u0645 \u067e\u0631\u0633\u067e\u0648\u0644\u06cc\u0633 \u0648 \u0627\u0633\u062a\u0642\u0644\u0627\u... | amirhossein1376/pft-clf-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| pft-clf-finetuned
=================
This model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on an "FarsNews1398" dataset. This dataset contains a collection of news that has been gathered from the farsnews website which is a news agency in Iran. You can download the dataset from here. I used category, ab... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_bat... |
fill-mask | transformers |
# nepbert
## Model description
Roberta trained from scratch on the Nepali CC-100 dataset with 12 million sentences.
## Intended uses & limitations
#### How to use
```python
from transformers import pipeline
pipe = pipeline(
"fill-mask",
model="amitness/nepbert",
tokenizer="amitness/nepbert"
)
print(p... | {"language": ["ne"], "license": "mit", "tags": ["roberta", "nepali-laguage-model"], "datasets": ["cc100"], "widget": [{"text": "\u0924\u093f\u092e\u0940\u0932\u093e\u0908 \u0915\u0938\u094d\u0924\u094b <mask>?"}]} | amitness/roberta-base-ne | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"nepali-laguage-model",
"ne",
"dataset:cc100",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ne"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #nepali-laguage-model #ne #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# nepbert
## Model description
Roberta trained from scratch on the Nepali CC-100 dataset with 12 million sentences.
## Intended uses & limitations
#### How to use
## Training data
The data was taken from the nepali language subset of CC-100 dataset.
## Training procedure
The model was trained on Google Colab ... | [
"# nepbert",
"## Model description\n\nRoberta trained from scratch on the Nepali CC-100 dataset with 12 million sentences.",
"## Intended uses & limitations",
"#### How to use",
"## Training data\n\nThe data was taken from the nepali language subset of CC-100 dataset.",
"## Training procedure\nThe model w... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #nepali-laguage-model #ne #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# nepbert",
"## Model description\n\nRoberta trained from scratch on the Nepali CC-100 dataset with 12 million sentences.",
"##... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Kannada
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kannada using the [OpenSLR SLR79](http://openslr.org/79/) dataset. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used dir... | {"language": "kn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Kannada by Amogh Gopadi", "results": [{"task": {"type": "automatic-speech-recognition", "name": ... | amoghsgopadi/wav2vec2-large-xlsr-kn | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"kn",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"kn"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #kn #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Kannada
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kannada using the OpenSLR SLR79 dataset. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows, assuming you have a dataset with Kannad... | [
"# Wav2Vec2-Large-XLSR-53-Kannada\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kannada using the OpenSLR SLR79 dataset. When 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, assuming you have a dataset ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #kn #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Kannada\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kannada using the OpenSLR ... |
fill-mask | transformers |
# roberta-cord19-1M7k
> This model is based on ***RoBERTa*** and was pre-trained on 1.7 million sentences.
The training corpus was papers taken from *Semantic Scholar*'s CORD-19 historical releases. Corpus size is `13k` papers, `~60M` tokens. ... | {"language": "en", "thumbnail": "https://github.githubassets.com/images/icons/emoji/unicode/2695.png", "widget": [{"text": "Lung infiltrates cause significant morbidity and mortality in immunocompromised <mask>."}, {"text": "Tuberculosis appears to be an important <mask> in endemic regions especially in the non-HIV, no... | amoux/roberta-cord19-1M7k | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-cord19-1M7k
![](URL
> This model is based on *RoBERTa* and was pre-trained on 1.7 million sentences.
The training corpus was papers taken from *Semantic Scholar*'s CORD-19 historical releases. Corpus size is '13k' papers, '~60M' tokens. I used the full-text '"body_text"' of the papers in training (details... | [
"# roberta-cord19-1M7k\n\n![](URL\n\n> This model is based on *RoBERTa* and was pre-trained on 1.7 million sentences.\n\nThe training corpus was papers taken from *Semantic Scholar*'s CORD-19 historical releases. Corpus size is '13k' papers, '~60M' tokens. I used the full-text '\"body_text\"' of the papers in train... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-cord19-1M7k\n\n
7-class NER English model using [Flair TransformerWordEmbeddings - distilroberta-base](https://github.com/flairNLP/flair/).
| **tag** | **meaning** |... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "12 sets of 2 minutes 38 minutes between each set"}]} | amtam0/timer-ner-en | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #region-us
| #### This model is used in the Speech Interval Timer app
7-class NER English model using Flair TransformerWordEmbeddings - distilroberta-base.
---
The dataset was created manually (perfectible). Sentences example :
| [
"#### This model is used in the Speech Interval Timer app\n\n\n7-class NER English model using Flair TransformerWordEmbeddings - distilroberta-base.\n\n\n\n\n\n---\n\n\nThe dataset was created manually (perfectible). Sentences example :"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #region-us \n",
"#### This model is used in the Speech Interval Timer app\n\n\n7-class NER English model using Flair TransformerWordEmbeddings - distilroberta-base.\n\n\n\n\n\n---\n\n\nThe dataset was created manually (perfectible). Sentences... |
token-classification | flair | #### This model is used in the [Speech Interval Timer app](https://medium.com/@amtam0/speech-interval-timer-app-using-transformers-1df8fa3821d5)
7-class NER French model using [Flair TransformerWordEmbeddings - camembert-base](https://github.com/flairNLP/flair/).
| **tag** | **meaning** |
|----... | {"language": "fr", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "g\u00e9n\u00e8re 27 s\u00e9ries de 54 seconde "}, {"text": " 9 cycles de 17 minute "}, {"text": "initie 17 sets de 44 secondes 297 minutes entre s\u00e9ries"}, {"text": " 13 sets de 88 secondes 225 minutes 49 ent... | amtam0/timer-ner-fr | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fr #region-us
| #### This model is used in the Speech Interval Timer app
7-class NER French model using Flair TransformerWordEmbeddings - camembert-base.
---
Synthetic dataset has been used (perfectible). Sentences example in the widget.
| [
"#### This model is used in the Speech Interval Timer app\n\n\n7-class NER French model using Flair TransformerWordEmbeddings - camembert-base.\n\n\n\n\n\n---\n\n\nSynthetic dataset has been used (perfectible). Sentences example in the widget."
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fr #region-us \n",
"#### This model is used in the Speech Interval Timer app\n\n\n7-class NER French model using Flair TransformerWordEmbeddings - camembert-base.\n\n\n\n\n\n---\n\n\nSynthetic dataset has been used (perfectible). Sentences examp... |
automatic-speech-recognition | 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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | anan0329/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
audio-classification | 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-adult-child-cls
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-adult-child-cls", "results": []}]} | anantoj/wav2vec2-adult-child-cls | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-adult-child-cls
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1713
* Accuracy: 0.9460
* F1: 0.9509
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size:... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-adult-child-cls
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-xls-r-300m-adult-child-cls", "results": []}]} | anantoj/wav2vec2-large-xlsr-53-adult-child-cls | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-adult-child-cls
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1755
* Accuracy: 0.9432
* F1: 0.9472
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-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=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size:... |
automatic-speech-recognition | 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 KR... | {"language": "ko", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["kresnik/zeroth_korean"], "model-index": [{"name": "Wav2Vec2 XLS-R 1B Korean", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | anantoj/wav2vec2-xls-r-1b-korean | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ko",
"dataset:kresnik/zeroth_korean",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ko #dataset-kresnik/zeroth_korean #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 KRESNIK/ZEROTH\_KOREAN - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0639
* Wer: 0.0449
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### 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 #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ko #dataset-kresnik/zeroth_korean #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were use... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-adult-child-cls
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-xls-r-300m-adult-child-cls", "results": []}]} | anantoj/wav2vec2-xls-r-300m-adult-child-cls | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-adult-child-cls
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1770
* Accuracy: 0.9404
* F1: 0.9440
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\... |
automatic-speech-recognition | 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 th... | {"language": ["zh-CN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "sv"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automa... | anantoj/wav2vec2-xls-r-300m-zh-CN | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"sv",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-CN"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sv #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - ZH-CN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8122
* Wer: 0.8392
* Cer: 0.2059
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### 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 #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sv #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice Corpus 4](https://commonvoice.mozilla.org/en/datasets) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": [{"common_voice": "Common Voice Corpus 4"}], "metrics": ["wer"], "model-index": [{"name": "Hasni XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognit... | anas/wav2vec2-large-xlsr-arabic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ar",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ar #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice Corpus 4 dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can b... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice Corpus 4 dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ar #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice Corpus 4 datas... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descr... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-2", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-4", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-4
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-42
This model is a fine-tuned version of [bert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-42\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"#... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-6", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-6
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-1024-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-1024-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-2", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-42\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"##... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-128-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## M... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## ... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-16-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-256-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-256-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-256-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-256-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-256-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-256-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-256-finetuned-squad-seed-2", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-256-finetuned-squad-seed-2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-256-finetuned-squad-seed-4", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-256-finetuned-squad-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-256-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-256-finetuned-squad-seed-6", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-256-finetuned-squad-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-6
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-256-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-256-finetuned-squad-seed-8", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-256-finetuned-squad-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-8
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-256-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-32-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-32-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-32-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-32-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-512-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-512-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-512-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descri... |
question-answering | 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-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-2\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-512-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-512-finetuned-squad-seed-4", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-4 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-4
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-4\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-512-finetuned-squad-seed-6
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-512-finetuned-squad-seed-6", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-6 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-6
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-6\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-512-finetuned-squad-seed-8
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-512-finetuned-squad-seed-8", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-512-finetuned-squad-seed-8 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-8
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-512-finetuned-squad-seed-8\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
question-answering | 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-few-shot-k-64-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-64-finetuned-squad-seed-0", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-64-finetuned-squad-seed-0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-0
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-0\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descript... |
question-answering | 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-few-shot-k-64-finetuned-squad-seed-10
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-few-shot-k-64-finetuned-squad-seed-10", "results": []}]} | anas-awadalla/bert-base-uncased-few-shot-k-64-finetuned-squad-seed-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-10
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-uncased-few-shot-k-64-finetuned-squad-seed-10\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model descrip... |
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