pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134453
- CO2 Emissions (in grams): 38.102565360610484
## Validation Metrics
- Loss: 0.172550767660141
- Accuracy: 0.9355
- Precision: 0.9362853135644159
- Recall: 0.9346
- AUC: 0.98267064
- F1: 0.9354418977079372
## Usage
You can us... | {"language": "en", "tags": "autonlp", "datasets": ["mmcquade11/autonlp-data-imdb-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 38.102565360610484} | mmcquade11/autonlp-imdb-test-21134453 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-imdb-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-mmcquade11/autonlp-data-imdb-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134453
- CO2 Emissions (in grams): 38.102565360610484
## Validation Metrics
- Loss: 0.172550767660141
- Accuracy: 0.9355
- Precision: 0.9362853135644159
- Recall: 0.9346
- AUC: 0.98267064
- F1: 0.9354418977079372
## Usage
You can us... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 21134453\n- CO2 Emissions (in grams): 38.102565360610484",
"## Validation Metrics\n\n- Loss: 0.172550767660141\n- Accuracy: 0.9355\n- Precision: 0.9362853135644159\n- Recall: 0.9346\n- AUC: 0.98267064\n- F1: 0.9354418977079372",
... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-mmcquade11/autonlp-data-imdb-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 21134453\n- CO2 Emissions (in grams... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133
- CO2 Emissions (in grams): 286.4350821612984
## Validation Metrics
- Loss: 1.1805976629257202
- Rouge1: 55.4013
- Rouge2: 30.8004
- RougeL: 52.57
- RougeLsum: 52.6103
- Gen Len: 15.3458
## Usage
You can use cURL to access this model... | {"language": "en", "tags": "autonlp", "datasets": ["mmcquade11/autonlp-data-reuters-summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 286.4350821612984} | mmcquade11/autonlp-reuters-summarization-34018133 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-reuters-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-mmcquade11/autonlp-data-reuters-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133
- CO2 Emissions (in grams): 286.4350821612984
## Validation Metrics
- Loss: 1.1805976629257202
- Rouge1: 55.4013
- Rouge2: 30.8004
- RougeL: 52.57
- RougeLsum: 52.6103
- Gen Len: 15.3458
## Usage
You can use cURL to access this model... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 34018133\n- CO2 Emissions (in grams): 286.4350821612984",
"## Validation Metrics\n\n- Loss: 1.1805976629257202\n- Rouge1: 55.4013\n- Rouge2: 30.8004\n- RougeL: 52.57\n- RougeLsum: 52.6103\n- Gen Len: 15.3458",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-mmcquade11/autonlp-data-reuters-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 34018133\n- CO2 Emi... |
text2text-generation | transformers |
This is an autoNLP model I trained on Reuters dataset
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133
- CO2 Emissions (in grams): 286.4350821612984
## Validation Metrics
- Loss: 1.1805976629257202
- Rouge1: 55.4013
- Rouge2: 30.8004
- RougeL: 52.57
- RougeLsum: 52.6103
- Gen Len: 1... | {"language": "en", "tags": "autonlp", "datasets": ["mmcquade11/autonlp-data-reuters-summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 286.4350821612984} | mmcquade11-test/reuters-summarization | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-reuters-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-mmcquade11/autonlp-data-reuters-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
This is an autoNLP model I trained on Reuters dataset
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133
- CO2 Emissions (in grams): 286.4350821612984
## Validation Metrics
- Loss: 1.1805976629257202
- Rouge1: 55.4013
- Rouge2: 30.8004
- RougeL: 52.57
- RougeLsum: 52.6103
- Gen Len: 1... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 34018133\n- CO2 Emissions (in grams): 286.4350821612984",
"## Validation Metrics\n\n- Loss: 1.1805976629257202\n- Rouge1: 55.4013\n- Rouge2: 30.8004\n- RougeL: 52.57\n- RougeLsum: 52.6103\n- Gen Len: 15.3458",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #en #dataset-mmcquade11/autonlp-data-reuters-summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 34018133\n- CO2 Emissions (in ... |
null | null | Save thhis here | {} | mmmarchio/Testmodel | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Save thhis here | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | # BERT Base Fine-tuned on MTSamples
This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
| {} | mnaylor/base-bert-finetuned-mtsamples | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT Base Fine-tuned on MTSamples
This model is BERT-base fine-tuned on the MTSamples dataset, with a classification task defined in this repo.
| [
"# BERT Base Fine-tuned on MTSamples\nThis model is BERT-base fine-tuned on the MTSamples dataset, with a classification task defined in this repo."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT Base Fine-tuned on MTSamples\nThis model is BERT-base fine-tuned on the MTSamples dataset, with a classification task defined in this repo."
] |
text-classification | transformers | # BigBird for Mortality Prediction
Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon
admission. Data prepared for this task as de... | {"license": "bigscience-openrail-m"} | mnaylor/bigbird-base-mimic-mortality | null | [
"transformers",
"pytorch",
"safetensors",
"big_bird",
"text-classification",
"license:bigscience-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #big_bird #text-classification #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #region-us
| # BigBird for Mortality Prediction
Starting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This
model seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon
admission. Data prepared for this task as de... | [
"# BigBird for Mortality Prediction\n\nStarting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission notes. This \nmodel seeks to predict whether a certain patient will expire within a given ICU stay, based on the text available upon \nadmission. Data prepared for this t... | [
"TAGS\n#transformers #pytorch #safetensors #big_bird #text-classification #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #region-us \n",
"# BigBird for Mortality Prediction\n\nStarting with Google's base BigBird model, we fine-tuned on binary mortality prediction in MIMIC admission not... |
text-classification | transformers | # BioClinical BERT Fine-tuned on MTSamples
This model is simply [Alsentzer's Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp). | {} | mnaylor/bioclinical-bert-finetuned-mtsamples | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # BioClinical BERT Fine-tuned on MTSamples
This model is simply Alsentzer's Bio_ClinicalBERT fine-tuned on the MTSamples dataset, with a classification task defined in this repo. | [
"# BioClinical BERT Fine-tuned on MTSamples\nThis model is simply Alsentzer's Bio_ClinicalBERT fine-tuned on the MTSamples dataset, with a classification task defined in this repo."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioClinical BERT Fine-tuned on MTSamples\nThis model is simply Alsentzer's Bio_ClinicalBERT fine-tuned on the MTSamples dataset, with a classification task defined in this repo."
] |
fill-mask | transformers | # PsychBERT
This domain adapted language model is pretrained from the `bert-base-cased` checkpoint on masked language modeling, using a dataset of ~40,000 PubMed papers in the domain of psychology, psychiatry, mental health, and behavioral health; as well as a dastaset of roughly 200,000 social media conversations abou... | {} | mnaylor/psychbert-cased | null | [
"transformers",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # PsychBERT
This domain adapted language model is pretrained from the 'bert-base-cased' checkpoint on masked language modeling, using a dataset of ~40,000 PubMed papers in the domain of psychology, psychiatry, mental health, and behavioral health; as well as a dastaset of roughly 200,000 social media conversations abou... | [
"# PsychBERT\nThis domain adapted language model is pretrained from the 'bert-base-cased' checkpoint on masked language modeling, using a dataset of ~40,000 PubMed papers in the domain of psychology, psychiatry, mental health, and behavioral health; as well as a dastaset of roughly 200,000 social media conversation... | [
"TAGS\n#transformers #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# PsychBERT\nThis domain adapted language model is pretrained from the 'bert-base-cased' checkpoint on masked language modeling, using a dataset of ~40,000 PubMed papers in the domain of psychology, psychiatry,... |
automatic-speech-recognition | transformers |
# Russian Speech Recognition model | {"language": ["ru"], "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "Russian Wav2Vec2 XLS-R 300m", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Co... | mobedkova/wav2vec2-large-xls-r-300m-ru | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ru",
"dataset:common_voice",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #model-index #endpoints_compatible #region-us
|
# Russian Speech Recognition model | [
"# Russian Speech Recognition model"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #model-index #endpoints_compatible #region-us \n",
"# Russian Speech Recognition model"
] |
text-generation | transformers |
# Dailo-GPT small Yukub model v3 | {"tags": ["conversational"]} | model-mili/DailoGPT-Yukub-v3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Dailo-GPT small Yukub model v3 | [
"# Dailo-GPT small Yukub model v3"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Dailo-GPT small Yukub model v3"
] |
text-generation | transformers |
# DialoGPT-small-Sapph-v1 | {"tags": ["conversational"]} | model-mili/DialoGPT-small-Sapph-v1 | 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
|
# DialoGPT-small-Sapph-v1 | [
"# DialoGPT-small-Sapph-v1"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-small-Sapph-v1"
] |
text-generation | transformers |
# Dialo-GPT small Yukub model v2 | {"tags": ["conversational"]} | model-mili/DialoGPT-small-Yukub-v2 | 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
|
# Dialo-GPT small Yukub model v2 | [
"# Dialo-GPT small Yukub model v2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dialo-GPT small Yukub model v2"
] |
text-generation | transformers |
# Dialo-GPT small Yukub model | {"tags": ["conversational"]} | model-mili/DialoGPT-small-Yukub | 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
|
# Dialo-GPT small Yukub model | [
"# Dialo-GPT small Yukub model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dialo-GPT small Yukub model"
] |
text-classification | transformers |
# BERT-ASTD Balanced
Arabic version bert model fine tuned on ASTD dataset balanced version to identify twitter sentiments in Arabic language MSA dialect .
## Data
The model were fine-tuned on ~1330 tweet in Arabic language.
## Results
| class | precision | recall | f1-score | Support |
|----------|-----------|--... | {"language": ["ar"], "tags": ["ASTD"], "datasets": ["ASTD"], "widget": [{"text": "\u0627\u0644\u0639\u0646\u0641 \u0648\u0627\u0644\u0642\u062a\u0644 \u0641\u064a \u0645\u062d\u064a\u0637 \u0627\u0644\u0639\u0627\u0644\u0645 \u0641\u064a \u0632\u064a\u0627\u062f\u0647 \u064a\u0648\u0645\u064a\u0627"}, {"text": "\u0627\... | mofawzy/BERT-ASTD | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ASTD",
"ar",
"dataset:ASTD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #ASTD #ar #dataset-ASTD #autotrain_compatible #endpoints_compatible #region-us
| BERT-ASTD Balanced
==================
Arabic version bert model fine tuned on ASTD dataset balanced version to identify twitter sentiments in Arabic language MSA dialect .
Data
----
The model were fine-tuned on ~1330 tweet in Arabic language.
Results
-------
How to use
----------
You can use these models b... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #ASTD #ar #dataset-ASTD #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# BERT-ASTD Balanced
Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from booking.com (HARD) dataset balanced version to identify sentiments opinion in Arabic language.
## Data
The model were fine-tuned on ~93000 book reviews in arabic using bert large arabic
Dataset:
- Train 70%
- Validation: ... | {"language": ["ar"], "tags": ["HARD"], "datasets": ["HARD"], "widget": [{"text": "\u062c\u064a\u062f. \u0627\u0644\u0645\u0643\u0627\u0646 \u062c\u0645\u064a\u0644 \u0648\u0647\u0627\u062f\u064a\u0621. \u0643\u0644 \u0634\u064a \u062c\u064a\u062f \u0648\u0646\u0638\u064a\u0641"}, {"text": "\u0627\u0633\u062a\u063a\u063... | mofawzy/Bert-hard-balanced | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"HARD",
"ar",
"dataset:HARD",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #HARD #ar #dataset-HARD #autotrain_compatible #endpoints_compatible #region-us
| BERT-ASTD Balanced
==================
Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from URL (HARD) dataset balanced version to identify sentiments opinion in Arabic language.
Data
----
The model were fine-tuned on ~93000 book reviews in arabic using bert large arabic
Dataset:
* Train 7... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #HARD #ar #dataset-HARD #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# argpt2-goodreads
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an goodreads LABR dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4389
## Model description
Generate sentences either positive/negative examples based on goodreads corpus in ar... | {"language": "ar", "tags": ["generated_from_trainer"], "datasets": ["LABR"], "widget": [{"text": "\u0643\u0627\u0646 \u0627\u0644\u0643\u0627\u062a\u0628 \u0645\u0645\u0643\u0646"}, {"text": "\u0643\u062a\u0627\u0628 \u0645\u0645\u062a\u0627\u0632 \u0648\u0644\u0643\u0646"}, {"text": "\u0631\u0648\u0627\u064a\u0629 \u0... | mofawzy/argpt2-goodreads | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"ar",
"dataset:LABR",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #ar #dataset-LABR #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# argpt2-goodreads
This model is a fine-tuned version of gpt2-medium on an goodreads LABR dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4389
## Model description
Generate sentences either positive/negative examples based on goodreads corpus in arabic language.
## Intended uses & lim... | [
"# argpt2-goodreads\n\nThis model is a fine-tuned version of gpt2-medium on an goodreads LABR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4389",
"## Model description\n\nGenerate sentences either positive/negative examples based on goodreads corpus in arabic language.",
"## In... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #ar #dataset-LABR #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# argpt2-goodreads\n\nThis model is a fine-tuned version of gpt2-medium on an goodreads LABR dataset.\nIt achieves the following r... |
text-classification | transformers |
# BERT-AJGT
Arabic version bert model fine tuned on AJGT dataset
## Data
The model were fine-tuned on ~1800 sentence from twitter for Jordanian dialect.
## Results
| class | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0 | 0.9462 | 0.9778 | 0.9617 ... | {"language": ["ar"], "tags": ["AJGT"], "datasets": ["AJGT"], "widget": [{"text": "\u064a\u0647\u062f\u064a \u0627\u0644\u0644\u0647 \u0645\u0646 \u064a\u0634\u0627\u0621"}, {"text": "\u0627\u0644\u0627\u0633\u0644\u0648\u0628 \u0642\u0630\u0631 \u0648\u0642\u0645\u0627\u0645\u0647"}]} | mofawzy/bert-ajgt | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"AJGT",
"ar",
"dataset:AJGT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #AJGT #ar #dataset-AJGT #autotrain_compatible #endpoints_compatible #region-us
| BERT-AJGT
=========
Arabic version bert model fine tuned on AJGT dataset
Data
----
The model were fine-tuned on ~1800 sentence from twitter for Jordanian dialect.
Results
-------
How to use
----------
You can use these models by installing 'torch' or 'tensorflow' and Huggingface library 'transformers'. And... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #AJGT #ar #dataset-AJGT #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# bert-arsentd-lev
Arabic version bert model fine tuned on ArSentD-LEV dataset
## Data
The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes we used 3 out of 5 int the experiment.
## Results
| class | precision | recall | f1-score | Support |
|----------|-----------... | {"language": ["ar"], "tags": ["ArSentD-LEV"], "datasets": ["ArSentD-LEV"], "widget": [{"text": "\u064a\u0647\u062f\u064a \u0627\u0644\u0644\u0647 \u0645\u0646 \u064a\u0634\u0627\u0621"}, {"text": "\u0627\u0644\u0627\u0633\u0644\u0648\u0628 \u0642\u0630\u0631 \u0648\u0642\u0645\u0627\u0645\u0647"}]} | mofawzy/bert-arsentd-lev | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"ArSentD-LEV",
"ar",
"dataset:ArSentD-LEV",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #ArSentD-LEV #ar #dataset-ArSentD-LEV #autotrain_compatible #endpoints_compatible #region-us
| bert-arsentd-lev
================
Arabic version bert model fine tuned on ArSentD-LEV dataset
Data
----
The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes we used 3 out of 5 int the experiment.
Results
-------
How to use
----------
You can use these models by install... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #ArSentD-LEV #ar #dataset-ArSentD-LEV #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# BERT-LABR unbalanced
Arabic version bert model fine tuned on LABR dataset
## Data
The model were fine-tuned on ~63000 book reviews in arabic using bert large arabic
## Results
| class | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0 | 0.8109 | 0.6... | {"language": ["ar"], "tags": ["labr"], "datasets": ["labr"], "widget": [{"text": "\u0643\u062a\u0627\u0628 \u0645\u0645\u0644 \u062c\u062f\u0627 \u062a\u0636\u064a\u064a\u0639 \u0648\u0642\u062a"}, {"text": "\u0627\u0633\u0644\u0648\u0628 \u0645\u0645\u062a\u0639 \u0648\u0634\u064a\u0642 \u0641\u064a \u0627\u0644\u0643... | mofawzy/bert-labr-unbalanced | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"labr",
"ar",
"dataset:labr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #labr #ar #dataset-labr #autotrain_compatible #endpoints_compatible #region-us
| BERT-LABR unbalanced
====================
Arabic version bert model fine tuned on LABR dataset
Data
----
The model were fine-tuned on ~63000 book reviews in arabic using bert large arabic
Results
-------
How to use
----------
You can use these models by installing 'torch' or 'tensorflow' and Huggingface li... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #labr #ar #dataset-labr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ### Generate Arabic reviews sentences with model GPT-2 Medium.
#### Load model
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("mofawzy/gpt-2-medium-ar")
model = AutoModelWithLMHead.from_pretrained("mofawzy/gpt-2-medium-ar")
```
### Eval:
```
***** eval... | {} | mofawzy/gpt-2-goodreads-ar | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Generate Arabic reviews sentences with model GPT-2 Medium.
#### Load model
### Eval:
#### Notebook:
URL
| [
"### Generate Arabic reviews sentences with model GPT-2 Medium.",
"#### Load model",
"### Eval:",
"#### Notebook:\nURL"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Generate Arabic reviews sentences with model GPT-2 Medium.",
"#### Load model",
"### Eval:",
"#### Notebook:\nURL"
] |
text-generation | transformers | ### GPT-2 Arabic Sentence Generator
Generate Reviews Sentences for Arabic.
language: "Arabic"
tags:
- Arabic
- generate text
- generate reviews
datasets:
- Large-scale book reviews Arabic LABR dataset.
#### Load Model
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pret... | {} | mofawzy/gpt2-arabic-sentence-generator | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### GPT-2 Arabic Sentence Generator
Generate Reviews Sentences for Arabic.
language: "Arabic"
tags:
- Arabic
- generate text
- generate reviews
datasets:
- Large-scale book reviews Arabic LABR dataset.
#### Load Model
'''
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pret... | [
"### GPT-2 Arabic Sentence Generator\nGenerate Reviews Sentences for Arabic.\n\nlanguage: \"Arabic\"\n\ntags:\n- Arabic\n- generate text\n- generate reviews\n\ndatasets:\n- Large-scale book reviews Arabic LABR dataset.",
"#### Load Model\n'''\nfrom transformers import AutoTokenizer, AutoModelWithLMHead\ntokenizer... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### GPT-2 Arabic Sentence Generator\nGenerate Reviews Sentences for Arabic.\n\nlanguage: \"Arabic\"\n\ntags:\n- Arabic\n- generate text\n- generate rev... |
null | null | # arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the... | {"language": "ar", "widget": [{"text": "\u0644\u0644\u0648\u0642\u0627\u064a\u0647 \u0645\u0646 \u0639\u062f\u0645 \u0627\u0646\u062a\u0634\u0627\u0631 [MASK]"}]} | moha/arabert_arabic_covid19 | null | [
"ar",
"arxiv:2004.04315",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04315"
] | [
"ar"
] | TAGS
#ar #arxiv-2004.04315 #region-us
| arabert\_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
=============================================================================================
ARABERT COVID-19 is a pretrained (fine-tuned) version of the AraBERT v2 model (URL The pretraining was done using 1.5 million mult... | [] | [
"TAGS\n#ar #arxiv-2004.04315 #region-us \n"
] |
fill-mask | transformers | # arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.... | {"language": "ar", "widget": [{"text": "\u0644\u0643\u064a \u0646\u062a\u062c\u0646\u0628 \u0641\u064a\u0631\u0648\u0633 [MASK]"}]} | moha/arabert_c19 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2105.03143",
"arxiv:2004.04315",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03143",
"2004.04315"
] | [
"ar"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ar #arxiv-2105.03143 #arxiv-2004.04315 #autotrain_compatible #endpoints_compatible #region-us
| arabert\_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
=============================================================================================
ARABERT COVID-19 Arxiv URL is a pretrained (fine-tuned) version of the AraBERT v2 model (URL The pretraining was done using 1.5 mi... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #arxiv-2105.03143 #arxiv-2004.04315 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # mbert_c19: An mbert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**mBERT COVID-19** [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the mBERT model (https://huggingface.co/bert-base-multilingual-cased). The pretraining was done using 1.5 million multi... | {"language": "ar", "widget": [{"text": "\u0644\u0644\u0648\u0642\u0627\u064a\u0647 \u0645\u0646 \u0627\u0646\u062a\u0634\u0627\u0631 [MASK]"}]} | moha/mbert_ar_c19 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2105.03143",
"arxiv:2004.04315",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.03143",
"2004.04315"
] | [
"ar"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ar #arxiv-2105.03143 #arxiv-2004.04315 #autotrain_compatible #endpoints_compatible #region-us
| mbert\_c19: An mbert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
=========================================================================================
mBERT COVID-19 Arxiv URL is a pretrained (fine-tuned) version of the mBERT model (URL The pretraining was done using 1.5 million multi-dia... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #arxiv-2105.03143 #arxiv-2004.04315 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the... | {"language": "ar", "widget": [{"text": "\u0644\u0644\u0648\u0642\u0627\u064a\u0647 \u0645\u0646 \u0639\u062f\u0645 \u0627\u0646\u062a\u0634\u0627\u0631 [MASK]"}]} | mohadz/arabert_arabic_covid19 | null | [
"transformers",
"bert",
"fill-mask",
"ar",
"arxiv:2004.04315",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04315"
] | [
"ar"
] | TAGS
#transformers #bert #fill-mask #ar #arxiv-2004.04315 #autotrain_compatible #endpoints_compatible #region-us
| arabert\_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
=============================================================================================
ARABERT COVID-19 is a pretrained (fine-tuned) version of the AraBERT v2 model (URL The pretraining was done using 1.5 million mult... | [] | [
"TAGS\n#transformers #bert #fill-mask #ar #arxiv-2004.04315 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# wav2vec-malayalam-checkpoint
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-malayalam-checkpoint", "results": []}]} | mohamed-illiyas/wav2vec-malayalam-checkpoint | 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
| wav2vec-malayalam-checkpoint
============================
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: 0.6457
* Wer: 0.6608
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0003\n* train\\_batch\\_size: 8... |
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. -->
# wav2vec-malayalam
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-malayalam", "results": []}]} | mohamed-illiyas/wav2vec-malayalam | 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
|
# wav2vec-malayalam
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m 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 hyperpara... | [
"# wav2vec-malayalam\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m 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 procedu... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec-malayalam\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model description\n\nMore inf... |
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-lj-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-lj-demo-colab", "results": []}]} | mohamed-illiyas/wav2vec2-base-lj-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-lj-demo-colab
===========================
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: 3.7050
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0001\n* train\\_batch\\_size: 3... |
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 5.1](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"], "metrics": ["wer"], "use datasets": [{"common_voice": "Common Voice Corpus 5.1"}], "model-index": [{"name": "Hasni XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-re... | mohamed1ai/wav2vec2-large-xls-ar | null | [
"transformers",
"pytorch",
"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 #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 5.1 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 ... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice Corpus 5.1 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\... | [
"TAGS\n#transformers #pytorch #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 5.1 dataset.... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5_small_summarization_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "t5_small_summarization_model", "results": []}]} | mohammadtari/arxivinterface | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5_small_summarization_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# t5_small_summarization_model\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5_small_summarization_model\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluatio... |
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 `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice)
and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus).
When ... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "arabic_speech_corpus"], "metrics": ["wer"], "model-index": [{"name": "Mohammed XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognit... | mohammed/ar | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ar",
"dataset:common_voice",
"dataset:arabic_speech_corpus",
"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 #dataset-common_voice #dataset-arabic_speech_corpus #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 'train' splits of Common Voice
and Arabic Speech Corpus.
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:
The ou... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non Arabic using the 'train' splits of Common Voice\nand Arabic Speech Corpus.\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 fo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ar #dataset-common_voice #dataset-arabic_speech_corpus #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xls... |
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 `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice)
and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus).
When ... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "arabic_speech_corpus"], "metrics": ["wer"], "model-index": [{"name": "Mohammed XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognit... | mohammed/wav2vec2-large-xlsr-arabic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ar",
"dataset:common_voice",
"dataset:arabic_speech_corpus",
"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 #dataset-common_voice #dataset-arabic_speech_corpus #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 'train' splits of Common Voice
and Arabic Speech Corpus.
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:
The ou... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non Arabic using the 'train' splits of Common Voice\nand Arabic Speech Corpus.\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 fo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ar #dataset-common_voice #dataset-arabic_speech_corpus #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xls... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | mohammedks713/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 | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Jon Snow DialoGPT Model | {"tags": ["conversational"]} | mohammedks713/DialoGPT-small-jonsnow | 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
|
# Jon Snow DialoGPT Model | [
"# Jon Snow DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jon Snow DialoGPT Model"
] |
text-classification | transformers | [BERT base model (uncased)](https://huggingface.co/bert-base-uncased) fine tuned on [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | {} | mohsenfayyaz/toxicity-classifier | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERT base model (uncased) fine tuned on Jigsaw Unintended Bias in Toxicity Classification | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
null | null |
# Wav2Vec2-Large-XLSR-53 | {"tags": ["xlsr-fine-tuning-week"]} | moja/EN-XLSR-Wav2Vec2 | null | [
"xlsr-fine-tuning-week",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#xlsr-fine-tuning-week #region-us
|
# Wav2Vec2-Large-XLSR-53 | [
"# Wav2Vec2-Large-XLSR-53"
] | [
"TAGS\n#xlsr-fine-tuning-week #region-us \n",
"# Wav2Vec2-Large-XLSR-53"
] |
null | transformers | To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the discriminator in transformers:
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("molly-hayward/bioelectra-b... | {} | molly-hayward/bioelectra-base-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us
| To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the discriminator in transformers:
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("molly-hayward/bioelectra-b... | [] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the generator in transformers:
from transformers import ElectraForMaskedLM, ElectraTokenizerFast
import torch
generator = ElectraForMaskedLM.from_pretrained("molly-hayward/bioelectra-base-generator"... | {} | molly-hayward/bioelectra-base-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us
| To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the generator in transformers:
from transformers import ElectraForMaskedLM, ElectraTokenizerFast
import torch
generator = ElectraForMaskedLM.from_pretrained("molly-hayward/bioelectra-base-generator"... | [] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the discriminator in transformers:
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("molly-hayward/bioelectra-s... | {} | molly-hayward/bioelectra-small-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us
| To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the discriminator in transformers:
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("molly-hayward/bioelectra-s... | [] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us \n"
] |
null | transformers | To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the generator in transformers:
from transformers import ElectraForMaskedLM, ElectraTokenizerFast
import torch
generator = ElectraForMaskedLM.from_pretrained("molly-hayward/bioelectra-small-generator... | {} | molly-hayward/bioelectra-small-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us
| To produce BioELECTRA, we pretrain ELECTRA on a corpus of over 20 million abstracts from PubMed.
How to use the generator in transformers:
from transformers import ElectraForMaskedLM, ElectraTokenizerFast
import torch
generator = ElectraForMaskedLM.from_pretrained("molly-hayward/bioelectra-small-generator... | [] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | Pre Träna CodeBert med JavaFx + Java FXML + JavaFx relaterat logik kod (dvs. Model, Controller för olika JavaFx kod).
Blev ungefär 130 k kod exemplar
````
***** train metrics *****
epoch = 3.0
train_loss = 0.4556
train_runtime = 5:57:43.71
train_sampl... | {} | moma1820/DSV-JavaFx-DAPT-CodeBert | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| Pre Träna CodeBert med JavaFx + Java FXML + JavaFx relaterat logik kod (dvs. Model, Controller för olika JavaFx kod).
Blev ungefär 130 k kod exemplar
' | [] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us \n"
] |
token-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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | momo/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0617
* Precision: 0.9262
* Recall: 0.9380
* F1: 0.9321
* Accuracy: 0.9840
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-generation | transformers | # kiosk_bot
KoGPT2를 이용한 간단한 kiosk chatbot 입니다.
데이터는 AiHub의 [한국어대화 데이터](https://aihub.or.kr/aidata/85)를 사용했습니다.
데이터는 학습만 진행하였고 공개는 하지 않습니다.
## Architecture
Hugging face의 예제들을 보며 구현하였습니다.
<img width="549" alt="gpt" src="https://user-images.githubusercontent.com/60643542/142431681-85db3d74-172d-45f0-9433-de43a8ae... | {} | momo/gpt2-kiosk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # kiosk_bot
KoGPT2를 이용한 간단한 kiosk chatbot 입니다.
데이터는 AiHub의 한국어대화 데이터를 사용했습니다.
데이터는 학습만 진행하였고 공개는 하지 않습니다.
## Architecture
Hugging face의 예제들을 보며 구현하였습니다.
<img width="549" alt="gpt" src="URL
은 로 구현하였다.
은 를 최대화 시키기위해 모델을 학습하였다.
## Install
## How to train?
기존 학습된 데이터로 대화를 하고 싶으시면 후 으로 넘어가셔도 됩니다.
##... | [
"# kiosk_bot\n\nKoGPT2를 이용한 간단한 kiosk chatbot 입니다. \n\n데이터는 AiHub의 한국어대화 데이터를 사용했습니다. \n\n데이터는 학습만 진행하였고 공개는 하지 않습니다.",
"## Architecture \nHugging face의 예제들을 보며 구현하였습니다. \n\n<img width=\"549\" alt=\"gpt\" src=\"URL\n\n 은 로 구현하였다. \n\n 은 를 최대화 시키기위해 모델을 학습하였다.",
"## Install",
"## How to train?\n기존 학습된 데이터로 대화... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# kiosk_bot\n\nKoGPT2를 이용한 간단한 kiosk chatbot 입니다. \n\n데이터는 AiHub의 한국어대화 데이터를 사용했습니다. \n\n데이터는 학습만 진행하였고 공개는 하지 않습니다.",
"## Architecture \nHugging face의 예제들을 보며 구현하였습니다. \n... |
token-classification | transformers |
# RiskData Brazilian Portuguese NER
## Model description
This is a finetunned version from [Neuralmind BERTimbau] (https://github.com/neuralmind-ai/portuguese-bert/blob/master/README.md) for Portuguese language.
## Intended uses & limitations
#### How to use
```python
from transformers import BertForTokenClassifi... | {"language": ["pt"], "tags": ["ner"], "metrics": ["f1", "accuracy", "precision", "recall"]} | monilouise/ner_news_portuguese | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"ner",
"pt",
"arxiv:1909.10649",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.10649"
] | [
"pt"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #ner #pt #arxiv-1909.10649 #autotrain_compatible #endpoints_compatible #region-us
|
# RiskData Brazilian Portuguese NER
## Model description
This is a finetunned version from [Neuralmind BERTimbau] (URL for Portuguese language.
## Intended uses & limitations
#### How to use
#### Limitations and bias
- The finetunned model was trained on a corpus with around 180 news articles crawled from Goog... | [
"# RiskData Brazilian Portuguese NER",
"## Model description\n\nThis is a finetunned version from [Neuralmind BERTimbau] (URL for Portuguese language.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n- The finetunned model was trained on a corpus with around 180 news arti... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #ner #pt #arxiv-1909.10649 #autotrain_compatible #endpoints_compatible #region-us \n",
"# RiskData Brazilian Portuguese NER",
"## Model description\n\nThis is a finetunned version from [Neuralmind BERTimbau] (URL for Portuguese language.",
"## Int... |
fill-mask | transformers |
# KoBigBird
<img src="https://user-images.githubusercontent.com/28896432/140442206-e34b02d5-e279-47e5-9c2a-db1278b1c14d.png" width="200"/>
Pretrained BigBird Model for Korean (**kobigbird-bert-base**)
## About
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to ... | {"language": "ko", "tags": ["korean"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | monologg/kobigbird-bert-base | null | [
"transformers",
"pytorch",
"safetensors",
"big_bird",
"fill-mask",
"korean",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #big_bird #fill-mask #korean #ko #autotrain_compatible #endpoints_compatible #region-us
|
# KoBigBird
<img src="URL width="200"/>
Pretrained BigBird Model for Korean (kobigbird-bert-base)
## About
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences.
BigBird relies on block sparse attention instead of normal attention (i.e. BERT... | [
"# KoBigBird\n\n<img src=\"URL width=\"200\"/>\n\nPretrained BigBird Model for Korean (kobigbird-bert-base)",
"## About\n\nBigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences.\n\nBigBird relies on block sparse attention instead of normal a... | [
"TAGS\n#transformers #pytorch #safetensors #big_bird #fill-mask #korean #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# KoBigBird\n\n<img src=\"URL width=\"200\"/>\n\nPretrained BigBird Model for Korean (kobigbird-bert-base)",
"## About\n\nBigBird, is a sparse-attention based transformer whic... |
null | transformers |
# KoELECTRA (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraMode... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | monologg/koelectra-base-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us
|
# KoELECTRA (Base Discriminator)
Pretrained ELECTRA Language Model for Korean ('koelectra-base-discriminator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForPreTraining
| [
"# KoELECTRA (Base Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForPreTraining"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us \n",
"# KoELECTRA (Base Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Lo... |
fill-mask | transformers |
# KoELECTRA (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, Elect... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | monologg/koelectra-base-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"korean",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #fill-mask #korean #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# KoELECTRA (Base Generator)
Pretrained ELECTRA Language Model for Korean ('koelectra-base-generator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForMaskedLM
| [
"# KoELECTRA (Base Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-generator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForMaskedLM"
] | [
"TAGS\n#transformers #pytorch #electra #fill-mask #korean #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# KoELECTRA (Base Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-generator')\n\nFor more detail, please see original repository.",
"## Usage... |
null | transformers |
# KoELECTRA v2 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v2-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import Elect... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | monologg/koelectra-base-v2-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us
|
# KoELECTRA v2 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean ('koelectra-base-v2-discriminator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForPreTraining
| [
"# KoELECTRA v2 (Base Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v2-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForPreTraining"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us \n",
"# KoELECTRA v2 (Base Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v2-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"... |
fill-mask | transformers |
# KoELECTRA v2 (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v2-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel,... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | monologg/koelectra-base-v2-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"korean",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #fill-mask #korean #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# KoELECTRA v2 (Base Generator)
Pretrained ELECTRA Language Model for Korean ('koelectra-base-v2-generator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForMaskedLM
| [
"# KoELECTRA v2 (Base Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v2-generator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForMaskedLM"
] | [
"TAGS\n#transformers #pytorch #electra #fill-mask #korean #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# KoELECTRA v2 (Base Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v2-generator')\n\nFor more detail, please see original repository.",
"##... |
null | transformers |
# KoELECTRA v3 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import Elect... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | monologg/koelectra-base-v3-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"korean",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us
|
# KoELECTRA v3 (Base Discriminator)
Pretrained ELECTRA Language Model for Korean ('koelectra-base-v3-discriminator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForPreTraining
| [
"# KoELECTRA v3 (Base Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v3-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForPreTraining"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us \n",
"# KoELECTRA v3 (Base Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v3-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"... |
fill-mask | transformers |
# KoELECTRA v3 (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel,... | {"language": "ko", "license": "apache-2.0", "tags": ["korean"]} | monologg/koelectra-base-v3-generator | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"fill-mask",
"korean",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #electra #fill-mask #korean #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# KoELECTRA v3 (Base Generator)
Pretrained ELECTRA Language Model for Korean ('koelectra-base-v3-generator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForMaskedLM
| [
"# KoELECTRA v3 (Base Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v3-generator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForMaskedLM"
] | [
"TAGS\n#transformers #pytorch #safetensors #electra #fill-mask #korean #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# KoELECTRA v3 (Base Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-base-v3-generator')\n\nFor more detail, please see original reposi... |
null | transformers |
# KoELECTRA (Small Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-small-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraMo... | {"language": "ko"} | monologg/koelectra-small-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us
|
# KoELECTRA (Small Discriminator)
Pretrained ELECTRA Language Model for Korean ('koelectra-small-discriminator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForPreTraining
| [
"# KoELECTRA (Small Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-small-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForPreTraining"
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us \n",
"# KoELECTRA (Small Discriminator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-small-discriminator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
... |
fill-mask | transformers |
# KoELECTRA (Small Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-small-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transformers import ElectraModel, Ele... | {"language": "ko"} | monologg/koelectra-small-generator | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us
|
# KoELECTRA (Small Generator)
Pretrained ELECTRA Language Model for Korean ('koelectra-small-generator')
For more detail, please see original repository.
## Usage
### Load model and tokenizer
### Tokenizer example
## Example using ElectraForMaskedLM
| [
"# KoELECTRA (Small Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-small-generator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and tokenizer",
"### Tokenizer example",
"## Example using ElectraForMaskedLM"
] | [
"TAGS\n#transformers #pytorch #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# KoELECTRA (Small Generator)\n\nPretrained ELECTRA Language Model for Korean ('koelectra-small-generator')\n\nFor more detail, please see original repository.",
"## Usage",
"### Load model and t... |
text-generation | transformers |
# ar-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in **first-person** Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:... | {"language": "ar"} | monsoon-nlp/ar-seq2seq-gender-decoder | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-generation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #bert #text-generation #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ar-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in first-person Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- '... | [
"# ar-seq2seq-gender (decoder)\n\nThis is a seq2seq model (decoder half) to \"flip\" gender in first-person Arabic sentences.\nThe model can augment your existing Arabic data, or generate counterfactuals\nto test a model's decisions (would changing the gender of the subject or speaker change output?).\n\nIntended E... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-generation #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ar-seq2seq-gender (decoder)\n\nThis is a seq2seq model (decoder half) to \"flip\" gender in first-person Arabic sentences.\nThe model can augment your existing Arabic dat... |
feature-extraction | transformers |
# ar-seq2seq-gender (encoder)
This is a seq2seq model (encoder half) to "flip" gender in **first-person** Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:... | {"language": "ar"} | monsoon-nlp/ar-seq2seq-gender-encoder | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"ar",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #ar #endpoints_compatible #has_space #region-us
|
# ar-seq2seq-gender (encoder)
This is a seq2seq model (encoder half) to "flip" gender in first-person Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- '... | [
"# ar-seq2seq-gender (encoder)\n\nThis is a seq2seq model (encoder half) to \"flip\" gender in first-person Arabic sentences.\nThe model can augment your existing Arabic data, or generate counterfactuals\nto test a model's decisions (would changing the gender of the subject or speaker change output?).\n\nIntended E... | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #ar #endpoints_compatible #has_space #region-us \n",
"# ar-seq2seq-gender (encoder)\n\nThis is a seq2seq model (encoder half) to \"flip\" gender in first-person Arabic sentences.\nThe model can augment your existing Arabic data, or generate counterfactu... |
null | transformers |
# Bangla-Electra
This is a second attempt at a Bangla/Bengali language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
**As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script**: h... | {"language": "bn"} | monsoon-nlp/bangla-electra | null | [
"transformers",
"pytorch",
"tf",
"electra",
"bn",
"arxiv:2004.07807",
"doi:10.57967/hf/1380",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.07807"
] | [
"bn"
] | TAGS
#transformers #pytorch #tf #electra #bn #arxiv-2004.07807 #doi-10.57967/hf/1380 #endpoints_compatible #region-us
|
# Bangla-Electra
This is a second attempt at a Bangla/Bengali language model trained with
Google Research's ELECTRA.
As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script: URL and URL
For causal language models, I would su... | [
"# Bangla-Electra\n\nThis is a second attempt at a Bangla/Bengali language model trained with\nGoogle Research's ELECTRA.\n\nAs of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and latinized script: URL and URL\n\nFor causal language models,... | [
"TAGS\n#transformers #pytorch #tf #electra #bn #arxiv-2004.07807 #doi-10.57967/hf/1380 #endpoints_compatible #region-us \n",
"# Bangla-Electra\n\nThis is a second attempt at a Bangla/Bengali language model trained with\nGoogle Research's ELECTRA.\n\nAs of 2022 I recommend Google's MuRIL model trained on English, ... |
feature-extraction | transformers |
# BERT-th
Adapted from https://github.com/ThAIKeras/bert for HuggingFace/Transformers library
## Pre-tokenization
You must run the original ThaiTokenizer to have your tokenization match that of the original model.
If you skip this step, you will not do much better than
mBERT or random chance!
[Refer to this CoLab... | {"language": "th"} | monsoon-nlp/bert-base-thai | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"th",
"arxiv:1609.08144",
"arxiv:1508.07909",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1609.08144",
"1508.07909"
] | [
"th"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #th #arxiv-1609.08144 #arxiv-1508.07909 #endpoints_compatible #region-us
| BERT-th
=======
Adapted from URL for HuggingFace/Transformers library
Pre-tokenization
----------------
You must run the original ThaiTokenizer to have your tokenization match that of the original model.
If you skip this step, you will not do much better than
mBERT or random chance!
Refer to this CoLab notebo... | [
"### Data Source\n\n\nTraining data for BERT-th come from the latest article dump of Thai Wikipedia on November 2, 2018. The raw texts are extracted by using WikiExtractor.",
"### Sentence Segmentation\n\n\nInput data need to be segmented into separate sentences before further processing by BERT modules. Since Th... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #th #arxiv-1609.08144 #arxiv-1508.07909 #endpoints_compatible #region-us \n",
"### Data Source\n\n\nTraining data for BERT-th come from the latest article dump of Thai Wikipedia on November 2, 2018. The raw texts are extracted by using Wiki... |
text2text-generation | transformers |
# byt5-base-dv
Pretrained from scratch on Dhivei (language of the Maldives)
with ByT5, Google's new byte-level tokenizer strategy.
**Use byt5-dv for now; this is less accurate**
Corpus: Sofwath's Dhivehi corpus https://github.com/Sofwath/DhivehiDatasets
Pretraining Notebook:
https://colab.research.google.com/driv... | {"language": "dv"} | monsoon-nlp/byt5-base-dv | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"dv",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #dv #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# byt5-base-dv
Pretrained from scratch on Dhivei (language of the Maldives)
with ByT5, Google's new byte-level tokenizer strategy.
Use byt5-dv for now; this is less accurate
Corpus: Sofwath's Dhivehi corpus URL
Pretraining Notebook:
URL
## Fine-tuning Demo
On Dhivehi news classification task
URL
## Issues
Th... | [
"# byt5-base-dv\n\nPretrained from scratch on Dhivei (language of the Maldives)\nwith ByT5, Google's new byte-level tokenizer strategy.\n\nUse byt5-dv for now; this is less accurate\n\nCorpus: Sofwath's Dhivehi corpus URL\n\nPretraining Notebook: \nURL",
"## Fine-tuning Demo\n\nOn Dhivehi news classification task... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #dv #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# byt5-base-dv\n\nPretrained from scratch on Dhivei (language of the Maldives)\nwith ByT5, Google's new byte-level tokenizer strategy.\n\nUse byt5-dv for now; thi... |
text2text-generation | transformers |
# byt5-basque
Pretrained from scratch on Euskara (Basque language)
with ByT5, Google's new byte-level tokenizer strategy.
Corpus: eu.wikipedia.org as of March 2020 (TFDS)
Pretraining Notebook: https://colab.research.google.com/drive/19Afq7CI6cOi1DaTpnQhBbEbnBzLSFHbH
## Todos
Fine-tuning
The Wikipedia corpus is ... | {"language": "eu"} | monsoon-nlp/byt5-basque | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"eu",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #eu #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# byt5-basque
Pretrained from scratch on Euskara (Basque language)
with ByT5, Google's new byte-level tokenizer strategy.
Corpus: URL as of March 2020 (TFDS)
Pretraining Notebook: URL
## Todos
Fine-tuning
The Wikipedia corpus is small for this language compared to web crawls. In the future I would add
OSCAR, if... | [
"# byt5-basque\n\nPretrained from scratch on Euskara (Basque language) \nwith ByT5, Google's new byte-level tokenizer strategy.\n\nCorpus: URL as of March 2020 (TFDS)\n\nPretraining Notebook: URL",
"## Todos\n\nFine-tuning\n\nThe Wikipedia corpus is small for this language compared to web crawls. In the future I ... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #eu #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# byt5-basque\n\nPretrained from scratch on Euskara (Basque language) \nwith ByT5, Google's new byte-level tokenizer strategy.\n\nCorpus: URL as of March 2020 (TF... |
text2text-generation | transformers |
# byt5-dv
Pretrained from scratch on Dhivei (language of the Maldives)
with ByT5, Google's new byte-level tokenizer strategy.
Corpus: dv.wikipedia.org as of March 2020 (TFDS)
Notebook - Pretraining on Wikipedia: https://colab.research.google.com/drive/19Afq7CI6cOi1DaTpnQhBbEbnBzLSFHbH
## Demo
Notebook - Finetunin... | {"language": "dv"} | monsoon-nlp/byt5-dv | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"dv",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #dv #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# byt5-dv
Pretrained from scratch on Dhivei (language of the Maldives)
with ByT5, Google's new byte-level tokenizer strategy.
Corpus: URL as of March 2020 (TFDS)
Notebook - Pretraining on Wikipedia: URL
## Demo
Notebook - Finetuning on Maldivian news classification task: URL
Current performance:
- mBERT: 52%
- ... | [
"# byt5-dv\n\nPretrained from scratch on Dhivei (language of the Maldives)\nwith ByT5, Google's new byte-level tokenizer strategy.\n\nCorpus: URL as of March 2020 (TFDS)\n\nNotebook - Pretraining on Wikipedia: URL",
"## Demo\n\nNotebook - Finetuning on Maldivian news classification task: URL\n\nCurrent performanc... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #dv #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# byt5-dv\n\nPretrained from scratch on Dhivei (language of the Maldives)\nwith ByT5, Google's new byte-level tokenizer strategy.\n\nCorpus: URL as of March 2020 ... |
text-generation | transformers |
# Dialect-AR-GPT-2021
## Finetuned AraGPT-2 demo
This model started with [AraGPT2-Medium](https://huggingface.co/aubmindlab/aragpt2-medium),
from AUB MIND Lab.
This model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins University / LREC for 10 epo... | {"language": "ar"} | monsoon-nlp/dialect-ar-gpt-2021 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15520"
] | [
"ar"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dialect-AR-GPT-2021
## Finetuned AraGPT-2 demo
This model started with AraGPT2-Medium,
from AUB MIND Lab.
This model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins University / LREC for 10 epochs.
You can use special tokens to prompt five dial... | [
"# Dialect-AR-GPT-2021",
"## Finetuned AraGPT-2 demo\n\nThis model started with AraGPT2-Medium,\nfrom AUB MIND Lab.\n\nThis model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,\nand Johns Hopkins University / LREC for 10 epochs.\n\nYou can use special tokens to... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dialect-AR-GPT-2021",
"## Finetuned AraGPT-2 demo\n\nThis model started with AraGPT2-Medium,\nfrom AUB MIND Lab.\n\nThis model wa... |
fill-mask | transformers |
# dv-labse
This is an experiment in cross-lingual transfer learning, to insert Dhivehi word and
word-piece tokens into Google's LaBSE model.
- Original model weights: https://huggingface.co/setu4993/LaBSE
- Original model announcement: https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html
This curr... | {"language": "dv"} | monsoon-nlp/dv-labse | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"dv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #dv #autotrain_compatible #endpoints_compatible #region-us
|
# dv-labse
This is an experiment in cross-lingual transfer learning, to insert Dhivehi word and
word-piece tokens into Google's LaBSE model.
- Original model weights: URL
- Original model announcement: URL
This currently outperforms dv-wave and dv-MuRIL (a similar transfer learning model) on
the Maldivian News Cla... | [
"# dv-labse\n\nThis is an experiment in cross-lingual transfer learning, to insert Dhivehi word and\nword-piece tokens into Google's LaBSE model.\n\n- Original model weights: URL\n- Original model announcement: URL\n\nThis currently outperforms dv-wave and dv-MuRIL (a similar transfer learning model) on \nthe Maldi... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #dv #autotrain_compatible #endpoints_compatible #region-us \n",
"# dv-labse\n\nThis is an experiment in cross-lingual transfer learning, to insert Dhivehi word and\nword-piece tokens into Google's LaBSE model.\n\n- Original model weights: URL\n- Original model a... |
fill-mask | transformers |
# dv-muril
This is an experiment in transfer learning, to insert Dhivehi word and
word-piece tokens into Google's MuRIL model.
This BERT-based model currently performs better than dv-wave ELECTRA on
the Maldivian News Classification task https://github.com/Sofwath/DhivehiDatasets
## Training
- Start with MuRIL (si... | {"language": "dv"} | monsoon-nlp/dv-muril | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"dv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #dv #autotrain_compatible #endpoints_compatible #region-us
|
# dv-muril
This is an experiment in transfer learning, to insert Dhivehi word and
word-piece tokens into Google's MuRIL model.
This BERT-based model currently performs better than dv-wave ELECTRA on
the Maldivian News Classification task URL
## Training
- Start with MuRIL (similar to mBERT) with no Thaana vocabula... | [
"# dv-muril\n\nThis is an experiment in transfer learning, to insert Dhivehi word and\nword-piece tokens into Google's MuRIL model.\n\nThis BERT-based model currently performs better than dv-wave ELECTRA on\nthe Maldivian News Classification task URL",
"## Training\n\n- Start with MuRIL (similar to mBERT) with no... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #dv #autotrain_compatible #endpoints_compatible #region-us \n",
"# dv-muril\n\nThis is an experiment in transfer learning, to insert Dhivehi word and\nword-piece tokens into Google's MuRIL model.\n\nThis BERT-based model currently performs better than dv-wave EL... |
null | transformers |
# dv-wave
This is a second attempt at a Dhivehi language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1ZJ3tU9MwyWj6UtQ-8G7QJKTn-hG1uQ9v?usp=sharing
Using SimpleTransformers to classify news ht... | {"language": "dv"} | monsoon-nlp/dv-wave | null | [
"transformers",
"pytorch",
"tf",
"electra",
"dv",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #tf #electra #dv #endpoints_compatible #region-us
|
# dv-wave
This is a second attempt at a Dhivehi language model trained with
Google Research's ELECTRA.
Tokenization and pre-training CoLab: URL
Using SimpleTransformers to classify news URL
V1: similar performance to mBERT on news classification task after finetuning for 3 epochs (52%)
V2: fixed tokenizers and ... | [
"# dv-wave\n\nThis is a second attempt at a Dhivehi language model trained with\nGoogle Research's ELECTRA.\n\nTokenization and pre-training CoLab: URL\n\nUsing SimpleTransformers to classify news URL\n\nV1: similar performance to mBERT on news classification task after finetuning for 3 epochs (52%)\n\nV2: fixed to... | [
"TAGS\n#transformers #pytorch #tf #electra #dv #endpoints_compatible #region-us \n",
"# dv-wave\n\nThis is a second attempt at a Dhivehi language model trained with\nGoogle Research's ELECTRA.\n\nTokenization and pre-training CoLab: URL\n\nUsing SimpleTransformers to classify news URL\n\nV1: similar performance t... |
text-generation | transformers |
# es-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in Spanish sentences.
The model can augment your existing Spanish data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- el profesor... | {"language": "es"} | monsoon-nlp/es-seq2seq-gender-decoder | null | [
"transformers",
"pytorch",
"bert",
"text-generation",
"es",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-generation #es #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# es-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in Spanish sentences.
The model can augment your existing Spanish data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- el profesor... | [
"# es-seq2seq-gender (decoder)\n\nThis is a seq2seq model (decoder half) to \"flip\" gender in Spanish sentences.\nThe model can augment your existing Spanish data, or generate counterfactuals\nto test a model's decisions (would changing the gender of the subject or speaker change output?).\n\nIntended Examples:\n\... | [
"TAGS\n#transformers #pytorch #bert #text-generation #es #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# es-seq2seq-gender (decoder)\n\nThis is a seq2seq model (decoder half) to \"flip\" gender in Spanish sentences.\nThe model can augment your existing Spanish data, or generate counterfa... |
feature-extraction | transformers |
# es-seq2seq-gender (encoder)
This is a seq2seq model (encoder half) to "flip" gender in Spanish sentences.
The model can augment your existing Spanish data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- el profesor... | {"language": "es"} | monsoon-nlp/es-seq2seq-gender-encoder | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"es",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #es #endpoints_compatible #has_space #region-us
|
# es-seq2seq-gender (encoder)
This is a seq2seq model (encoder half) to "flip" gender in Spanish sentences.
The model can augment your existing Spanish data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- el profesor... | [
"# es-seq2seq-gender (encoder)\n\nThis is a seq2seq model (encoder half) to \"flip\" gender in Spanish sentences.\nThe model can augment your existing Spanish data, or generate counterfactuals\nto test a model's decisions (would changing the gender of the subject or speaker change output?).\n\nIntended Examples:\n\... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #es #endpoints_compatible #has_space #region-us \n",
"# es-seq2seq-gender (encoder)\n\nThis is a seq2seq model (encoder half) to \"flip\" gender in Spanish sentences.\nThe model can augment your existing Spanish data, or generate counterfac... |
text-generation | transformers | # GPT-NYC-affirmations
## About
GPT2 (small version on HF) fine-tuned on questions and responses from https://reddit.com/r/asknyc
and then 2 epochs of [Value Affirmations](https://gist.github.com/mapmeld/c16794ecd93c241a4d6a65bda621bb55)
based on the OpenAI post [Improving Language Model Behavior](https://openai.com/... | {} | monsoon-nlp/gpt-nyc-affirmations | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-NYC-affirmations
## About
GPT2 (small version on HF) fine-tuned on questions and responses from URL
and then 2 epochs of Value Affirmations
based on the OpenAI post Improving Language Model Behavior
and corresponding paper.
Try prompting with or
I filtered AskNYC comments to ones with scores >= 3, and resp... | [
"# GPT-NYC-affirmations",
"## About\n\nGPT2 (small version on HF) fine-tuned on questions and responses from URL\nand then 2 epochs of Value Affirmations\nbased on the OpenAI post Improving Language Model Behavior\nand corresponding paper.\n\nTry prompting with or \n\nI filtered AskNYC comments to ones with sco... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-NYC-affirmations",
"## About\n\nGPT2 (small version on HF) fine-tuned on questions and responses from URL\nand then 2 epochs of Value Affirmations\nbase... |
text-generation | transformers | # GPT-NYC-nontoxic
## About
GPT2 (small version on HF) fine-tuned on questions and responses from https://reddit.com/r/asknyc
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I also added many tokens which were common on /r/AskNYC... | {} | monsoon-nlp/gpt-nyc-nontoxic | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-NYC-nontoxic
## About
GPT2 (small version on HF) fine-tuned on questions and responses from URL
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I also added many tokens which were common on /r/AskNYC but missing from
GPT2.
... | [
"# GPT-NYC-nontoxic",
"## About\n\nGPT2 (small version on HF) fine-tuned on questions and responses from URL\n\nI filtered comments to ones with scores >= 3, and responding directly\nto the original post ( = ignoring responses to other commenters).\nI also added many tokens which were common on /r/AskNYC but miss... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-NYC-nontoxic",
"## About\n\nGPT2 (small version on HF) fine-tuned on questions and responses from URL\n\nI filtered comments to ones with scores >= 3, and responding... |
text-generation | transformers | # GPT-NYC-small
## About
GPT2 (small version on HF) fine-tuned on questions and responses from https://reddit.com/r/asknyc
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I also added many tokens which were common on /r/AskNYC bu... | {"language": ["en"], "license": "mit", "tags": ["nyc", "reddit"], "datasets": ["monsoon-nlp/asknyc-chatassistant-format"], "pipeline_tag": "text-generation"} | monsoon-nlp/gpt-nyc-small | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"nyc",
"reddit",
"en",
"dataset:monsoon-nlp/asknyc-chatassistant-format",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #nyc #reddit #en #dataset-monsoon-nlp/asknyc-chatassistant-format #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT-NYC-small
## About
GPT2 (small version on HF) fine-tuned on questions and responses from URL
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I also added many tokens which were common on /r/AskNYC but missing from
GPT2.
Th... | [
"# GPT-NYC-small",
"## About\n\nGPT2 (small version on HF) fine-tuned on questions and responses from URL\n\nI filtered comments to ones with scores >= 3, and responding directly\nto the original post ( = ignoring responses to other commenters).\nI also added many tokens which were common on /r/AskNYC but missing... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #nyc #reddit #en #dataset-monsoon-nlp/asknyc-chatassistant-format #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT-NYC-small",
"## About\n\nGPT2 (small version on HF) fine-tuned on questions and r... |
text-generation | transformers | # GPT-NYC
## About
GPT2-Medium fine-tuned on questions and responses from https://reddit.com/r/asknyc
**2023 Update: try a larger model: [monsoon-nlp/nyc-savvy-llama2-7b](https://huggingface.co/monsoon-nlp/nyc-savvy-llama2-7b)**
I filtered comments to ones with scores >= 3, and responding directly
to the original p... | {"language": ["en"], "license": "mit", "tags": ["nyc", "reddit"], "datasets": ["monsoon-nlp/asknyc-chatassistant-format"], "pipeline_tag": "text-generation"} | monsoon-nlp/gpt-nyc | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"nyc",
"reddit",
"en",
"dataset:monsoon-nlp/asknyc-chatassistant-format",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #nyc #reddit #en #dataset-monsoon-nlp/asknyc-chatassistant-format #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT-NYC
## About
GPT2-Medium fine-tuned on questions and responses from URL
2023 Update: try a larger model: monsoon-nlp/nyc-savvy-llama2-7b
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I added tokens to match NYC neighbor... | [
"# GPT-NYC",
"## About\n\nGPT2-Medium fine-tuned on questions and responses from URL\n\n2023 Update: try a larger model: monsoon-nlp/nyc-savvy-llama2-7b\n\nI filtered comments to ones with scores >= 3, and responding directly\nto the original post ( = ignoring responses to other commenters).\n\nI added tokens to ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #nyc #reddit #en #dataset-monsoon-nlp/asknyc-chatassistant-format #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT-NYC",
"## About\n\nGPT2-Medium fine-tuned on questions an... |
feature-extraction | transformers |
# Releasing Hindi ELECTRA model
This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra).
**As of 2022 I recommend Google's MuRIL model trained on English, Hindi, and other major Indian languages, both in their script and latinized script*... | {"language": "hi"} | monsoon-nlp/hindi-bert | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"feature-extraction",
"hi",
"doi:10.57967/hf/1305",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #feature-extraction #hi #doi-10.57967/hf/1305 #endpoints_compatible #has_space #region-us
|
# Releasing Hindi ELECTRA model
This is a first attempt at a Hindi language model trained with Google Research's ELECTRA.
As of 2022 I recommend Google's MuRIL model trained on English, Hindi, and other major Indian languages, both in their script and latinized script: URL and URL
For causal language models, I woul... | [
"# Releasing Hindi ELECTRA model\n\nThis is a first attempt at a Hindi language model trained with Google Research's ELECTRA.\n\nAs of 2022 I recommend Google's MuRIL model trained on English, Hindi, and other major Indian languages, both in their script and latinized script: URL and URL\n\nFor causal language mode... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #feature-extraction #hi #doi-10.57967/hf/1305 #endpoints_compatible #has_space #region-us \n",
"# Releasing Hindi ELECTRA model\n\nThis is a first attempt at a Hindi language model trained with Google Research's ELECTRA.\n\nAs of 2022 I recommend Google's Mu... |
feature-extraction | transformers |
# Hindi language model
## Trained with ELECTRA base size settings
<a href="https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_">Tokenization and training CoLab</a>
## Example Notebooks
This model outperforms Multilingual BERT on <a href="https://colab.research.google.com/drive/1UYn5Th8u7xISnPU... | {"language": "hi"} | monsoon-nlp/hindi-tpu-electra | null | [
"transformers",
"pytorch",
"tf",
"electra",
"feature-extraction",
"hi",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #tf #electra #feature-extraction #hi #endpoints_compatible #region-us
|
# Hindi language model
## Trained with ELECTRA base size settings
<a href="URL and training CoLab</a>
## Example Notebooks
This model outperforms Multilingual BERT on <a href="URL movie reviews / sentiment analysis</a> (using SimpleTransformers)
You can get higher accuracy using ktrain + TensorFlow, where you can ... | [
"# Hindi language model",
"## Trained with ELECTRA base size settings\n\n<a href=\"URL and training CoLab</a>",
"## Example Notebooks\n\nThis model outperforms Multilingual BERT on <a href=\"URL movie reviews / sentiment analysis</a> (using SimpleTransformers)\n\nYou can get higher accuracy using ktrain + Tenso... | [
"TAGS\n#transformers #pytorch #tf #electra #feature-extraction #hi #endpoints_compatible #region-us \n",
"# Hindi language model",
"## Trained with ELECTRA base size settings\n\n<a href=\"URL and training CoLab</a>",
"## Example Notebooks\n\nThis model outperforms Multilingual BERT on <a href=\"URL movie revi... |
fill-mask | transformers |
## MuRIL - Unofficial
Multilingual Representations for Indian Languages : Google open sourced
this BERT model pre-trained on 17 Indian languages, and their transliterated
counterparts.
The model was trained using a self-supervised masked language modeling task. We do whole word masking with a maximum of 80 predictio... | {"language": ["en", "hi", "bn", "ta", "as", "gu", "kn", "ks", "ml", "mr", "ne", "or", "pa", "sa", "sd", "te", "ur", "multilingual"], "license": "apache-2.0"} | monsoon-nlp/muril-adapted-local | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"hi",
"bn",
"ta",
"as",
"gu",
"kn",
"ks",
"ml",
"mr",
"ne",
"or",
"pa",
"sa",
"sd",
"te",
"ur",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"hi",
"bn",
"ta",
"as",
"gu",
"kn",
"ks",
"ml",
"mr",
"ne",
"or",
"pa",
"sa",
"sd",
"te",
"ur",
"multilingual"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #en #hi #bn #ta #as #gu #kn #ks #ml #mr #ne #or #pa #sa #sd #te #ur #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MuRIL - Unofficial
Multilingual Representations for Indian Languages : Google open sourced
this BERT model pre-trained on 17 Indian languages, and their transliterated
counterparts.
The model was trained using a self-supervised masked language modeling task. We do whole word masking with a maximum of 80 predictio... | [
"## MuRIL - Unofficial\n\nMultilingual Representations for Indian Languages : Google open sourced\nthis BERT model pre-trained on 17 Indian languages, and their transliterated\ncounterparts.\n\nThe model was trained using a self-supervised masked language modeling task. We do whole word masking with a maximum of 80... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #en #hi #bn #ta #as #gu #kn #ks #ml #mr #ne #or #pa #sa #sd #te #ur #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MuRIL - Unofficial\n\nMultilingual Representations for Indian Languages : Googl... |
text-generation | transformers |
# no-phone-gpt2
This is a test to remove memorized private information, such as phone numbers, from a small GPT-2 model. This should not generate valid phone numbers.
Inspired by BAIR privacy research:
- https://bair.berkeley.edu/blog/2019/08/13/memorization/
- https://bair.berkeley.edu/blog/2020/12/20/lmmem/
[Blog... | {"language": "en", "license": "mit", "tags": ["exbert"]} | monsoon-nlp/no-phone-gpt2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# no-phone-gpt2
This is a test to remove memorized private information, such as phone numbers, from a small GPT-2 model. This should not generate valid phone numbers.
Inspired by BAIR privacy research:
- URL
- URL
Blog post
## Process
- All +## and +### tokens were replaced with new, randomly-selected 2- and 3-di... | [
"# no-phone-gpt2\n\nThis is a test to remove memorized private information, such as phone numbers, from a small GPT-2 model. This should not generate valid phone numbers.\n\nInspired by BAIR privacy research:\n- URL\n- URL\n\nBlog post",
"## Process\n\n- All +## and +### tokens were replaced with new, randomly-se... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# no-phone-gpt2\n\nThis is a test to remove memorized private information, such as phone numbers, from a small GPT-2 model. This s... |
text-generation | transformers |
# Sanaa-Dialect
## Finetuned Arabic GPT-2 demo
This is a small GPT-2 model, originally trained on Arabic Wikipedia circa September 2020 ,
finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins University / LREC
- https://qspace.qu.edu.qa/handle/10576/15265
- http... | {"language": "ar"} | monsoon-nlp/sanaa-dialect | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sanaa-Dialect
## Finetuned Arabic GPT-2 demo
This is a small GPT-2 model, originally trained on Arabic Wikipedia circa September 2020 ,
finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins University / LREC
- URL
- URL
- URL
You can use special tokens to pro... | [
"# Sanaa-Dialect",
"## Finetuned Arabic GPT-2 demo\n\nThis is a small GPT-2 model, originally trained on Arabic Wikipedia circa September 2020 ,\nfinetuned on dialect datasets from Qatar University, University of British Columbia / NLP,\nand Johns Hopkins University / LREC\n\n- URL\n- URL\n- URL\n\nYou can use sp... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sanaa-Dialect",
"## Finetuned Arabic GPT-2 demo\n\nThis is a small GPT-2 model, originally trained on Arabic Wikipedia circa September 2020 ,\nfine... |
text-generation | transformers |
# Sanaa
## Arabic GPT-2 demo
This is a small GPT-2 model retrained on Arabic Wikipedia circa September 2020
(due to memory limits, the first 600,000 lines of the Wiki dump)
There is NO content filtering in the current version; do not use for public-facing
text generation.
## Training
Training notebook: https://col... | {"language": "ar"} | monsoon-nlp/sanaa | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sanaa
## Arabic GPT-2 demo
This is a small GPT-2 model retrained on Arabic Wikipedia circa September 2020
(due to memory limits, the first 600,000 lines of the Wiki dump)
There is NO content filtering in the current version; do not use for public-facing
text generation.
## Training
Training notebook: URL
Steps ... | [
"# Sanaa",
"## Arabic GPT-2 demo\n\nThis is a small GPT-2 model retrained on Arabic Wikipedia circa September 2020\n(due to memory limits, the first 600,000 lines of the Wiki dump)\n\nThere is NO content filtering in the current version; do not use for public-facing\ntext generation.",
"## Training\n\nTraining ... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #ar #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sanaa",
"## Arabic GPT-2 demo\n\nThis is a small GPT-2 model retrained on Arabic Wikipedia circa September 2020\n(due to memory limits, the first 600,000 lines ... |
feature-extraction | transformers |
# TaMillion
This is the second version of a Tamil language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1Pwia5HJIb6Ad4Hvbx5f-IjND-vCaJzSE?usp=sharing
V1: small model with GPU; 190,000 steps;
... | {"language": "ta"} | monsoon-nlp/tamillion | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"electra",
"feature-extraction",
"ta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ta"
] | TAGS
#transformers #pytorch #tf #safetensors #electra #feature-extraction #ta #endpoints_compatible #region-us
|
# TaMillion
This is the second version of a Tamil language model trained with
Google Research's ELECTRA.
Tokenization and pre-training CoLab: URL
V1: small model with GPU; 190,000 steps;
V2 (current): base model with TPU and larger corpus; 224,000 steps
## Classification
Sudalai Rajkumar's Tamil-NLP page contain... | [
"# TaMillion\n\nThis is the second version of a Tamil language model trained with\nGoogle Research's ELECTRA.\n\nTokenization and pre-training CoLab: URL\n\nV1: small model with GPU; 190,000 steps;\n\nV2 (current): base model with TPU and larger corpus; 224,000 steps",
"## Classification\n\nSudalai Rajkumar's Tam... | [
"TAGS\n#transformers #pytorch #tf #safetensors #electra #feature-extraction #ta #endpoints_compatible #region-us \n",
"# TaMillion\n\nThis is the second version of a Tamil language model trained with\nGoogle Research's ELECTRA.\n\nTokenization and pre-training CoLab: URL\n\nV1: small model with GPU; 190,000 steps... |
text-classification | transformers | This is the *best performing* model used in the paper: "End-to-end Training For Financial Report Summarization"
https://www.aclweb.org/anthology/2020.fnp-1.20/ | {} | morenolq/SumTO_FNS2020 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This is the *best performing* model used in the paper: "End-to-end Training For Financial Report Summarization"
URL | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the BBC News Summary dataset (https://www.kaggle.com/pariza/bbc-news-summary).
The model has been generated as part of the in-lab practice of **Deep NLP course** currently held at Politecnico ... | {"base_model": "sshleifer/distilbart-cnn-12-6"} | morenolq/distilbart-bbc | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"base_model:sshleifer/distilbart-cnn-12-6",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #base_model-sshleifer/distilbart-cnn-12-6 #autotrain_compatible #endpoints_compatible #region-us
| This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the BBC News Summary dataset (URL
The model has been generated as part of the in-lab practice of Deep NLP course currently held at Politecnico di Torino.
Training parameters:
- 'num_train_epochs=2'
- 'fp16=True'
- 'per_device_train_batch_size=1'
-... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #base_model-sshleifer/distilbart-cnn-12-6 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | Accuracy = 92 | {} | moshew/bert-small-aug-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Accuracy = 92 | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \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. -->
# miny-bert-aug-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/go... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["augmented_glue_sst2"], "metrics": ["accuracy"], "model-index": [{"name": "miny-bert-aug-sst2-distilled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "augmented_glue_sst2", "type": "au... | moshew/miny-bert-aug-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:augmented_glue_sst2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-augmented_glue_sst2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| miny-bert-aug-sst2-distilled
============================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-256\_A-4 on the augmented\_glue\_sst2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2643
* Accuracy: 0.9128
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-augmented_glue_sst2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
text-classification | transformers | {'test_accuracy': 0.911697247706422,
'test_loss': 0.24090610444545746,
'test_runtime': 0.4372,
'test_samples_per_second': 1994.475,
'test_steps_per_second': 16.011} | {} | moshew/minylm-L3-aug-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| {'test_accuracy': 0.911697247706422,
'test_loss': 0.24090610444545746,
'test_runtime': 0.4372,
'test_samples_per_second': 1994.475,
'test_steps_per_second': 16.011} | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | {'test_accuracy': 0.9426605504587156,
'test_loss': 0.1693699210882187,
'test_runtime': 1.7713,
'test_samples_per_second': 492.29,
'test_steps_per_second': 3.952} | {} | moshew/mpnet-base-sst2-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"mpnet",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mpnet #text-classification #autotrain_compatible #endpoints_compatible #region-us
| {'test_accuracy': 0.9426605504587156,
'test_loss': 0.1693699210882187,
'test_runtime': 1.7713,
'test_samples_per_second': 492.29,
'test_steps_per_second': 3.952} | [] | [
"TAGS\n#transformers #pytorch #tensorboard #mpnet #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # Flaubert-base-cased-ecology_crisis
An adapted [__Flaubert/Flaubert_base-cased model__](https://github.com/getalp/Flaubert) Trained further on a Language modeling Task of unlabeled French tweets used to create the [CrisisDataset](https://github.com/DiegoKoz/french_ecological_crisis), The intermediate task of masqued ... | {} | moumeneb1/flaubert-base-cased-ecology_crisis | null | [
"transformers",
"flaubert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #flaubert #feature-extraction #endpoints_compatible #region-us
| # Flaubert-base-cased-ecology_crisis
An adapted __Flaubert/Flaubert_base-cased model__ Trained further on a Language modeling Task of unlabeled French tweets used to create the CrisisDataset, The intermediate task of masqued language modeling helped us improve the results on our paper compared to the standard flaubert... | [
"# Flaubert-base-cased-ecology_crisis\n\nAn adapted __Flaubert/Flaubert_base-cased model__ Trained further on a Language modeling Task of unlabeled French tweets used to create the CrisisDataset, The intermediate task of masqued language modeling helped us improve the results on our paper compared to the standard f... | [
"TAGS\n#transformers #flaubert #feature-extraction #endpoints_compatible #region-us \n",
"# Flaubert-base-cased-ecology_crisis\n\nAn adapted __Flaubert/Flaubert_base-cased model__ Trained further on a Language modeling Task of unlabeled French tweets used to create the CrisisDataset, The intermediate task of masq... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on CommonVoice Kinyarwanda (No LM)
This repository provides all the... | {"language": "rw", "license": "apache-2.0", "tags": ["CTC", "Attention", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | moumeneb1/testing | null | [
"speechbrain",
"wav2vec2",
"CTC",
"Attention",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"rw",
"dataset:commonvoice",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"rw"
] | TAGS
#speechbrain #wav2vec2 #CTC #Attention #pytorch #Transformer #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on CommonVoice Kinyarwanda (No LM)
=========================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (Kinyarwanda Language... | [
"### Transcribing your own audio files (in Kinyarwanda)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure ou... | [
"TAGS\n#speechbrain #wav2vec2 #CTC #Attention #pytorch #Transformer #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in Kinyarwanda)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={... |
summarization | transformers | ### Barthez model finetuned on orangeSum (abstract generation)
finetuning: examples/seq2seq (as of Feb 08 2021)
paper: https://arxiv.org/abs/2010.12321 \
github: https://github.com/moussaKam/BARThez
```
@article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={E... | {"language": ["fr"], "license": "apache-2.0", "tags": ["summarization", "bart"], "widget": [{"text": "Citant les pr\u00e9occupations de ses clients d\u00e9non\u00e7ant des cas de censure apr\u00e8s la suppression du compte de Trump, un fournisseur d'acc\u00e8s Internet de l'\u00c9tat de l'Idaho a d\u00e9cid\u00e9 de bl... | moussaKam/barthez-orangesum-abstract | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"bart",
"fr",
"arxiv:2010.12321",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12321"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #bart #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Barthez model finetuned on orangeSum (abstract generation)
finetuning: examples/seq2seq (as of Feb 08 2021)
paper: URL \
github: URL
| [
"### Barthez model finetuned on orangeSum (abstract generation)\n\nfinetuning: examples/seq2seq (as of Feb 08 2021)\n\npaper: URL \\\ngithub: URL"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #bart #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Barthez model finetuned on orangeSum (abstract generation)\n\nfinetuning: examples/seq2seq (as of Feb 08 2021)\n\npa... |
summarization | transformers | ### Barthez model finetuned on orangeSum (title generation)
finetuning: examples/seq2seq/ (as of Nov 06, 2020)
Metrics: ROUGE-2 > 23
paper: https://arxiv.org/abs/2010.12321 \
github: https://github.com/moussaKam/BARThez
```
@article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequen... | {"language": ["fr"], "license": "apache-2.0", "tags": ["summarization"], "widget": [{"text": "Citant les pr\u00e9occupations de ses clients d\u00e9non\u00e7ant des cas de censure apr\u00e8s la suppression du compte de Trump, un fournisseur d'acc\u00e8s Internet de l'\u00c9tat de l'Idaho a d\u00e9cid\u00e9 de bloquer Fa... | moussaKam/barthez-orangesum-title | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"fr",
"arxiv:2010.12321",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12321"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### Barthez model finetuned on orangeSum (title generation)
finetuning: examples/seq2seq/ (as of Nov 06, 2020)
Metrics: ROUGE-2 > 23
paper: URL \
github: URL
| [
"### Barthez model finetuned on orangeSum (title generation)\n\nfinetuning: examples/seq2seq/ (as of Nov 06, 2020)\n\nMetrics: ROUGE-2 > 23\n\npaper: URL \\\ngithub: URL"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Barthez model finetuned on orangeSum (title generation)\n\nfinetuning: examples/seq2seq/ (as of Nov 06, 2020)\n\nMetrics: ROUGE-2 > 2... |
text-classification | transformers | ### Barthez model finetuned on opinion classification task.
paper: https://arxiv.org/abs/2010.12321 \
github: https://github.com/moussaKam/BARThez
```
@article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazir... | {"language": ["fr"], "license": "apache-2.0", "tags": ["text-classification", "bart"], "widget": [{"text": "Barthez est le meilleur gardien du monde."}]} | moussaKam/barthez-sentiment-classification | null | [
"transformers",
"pytorch",
"mbart",
"text-classification",
"bart",
"fr",
"arxiv:2010.12321",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12321"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text-classification #bart #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Barthez model finetuned on opinion classification task.
paper: URL \
github: URL
| [
"### Barthez model finetuned on opinion classification task.\n\npaper: URL \\\ngithub: URL"
] | [
"TAGS\n#transformers #pytorch #mbart #text-classification #bart #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Barthez model finetuned on opinion classification task.\n\npaper: URL \\\ngithub: URL"
] |
fill-mask | transformers | A french sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large). <br>
BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining. <br>
Unlike already existing BERT-based French language ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["summarization", "bart"], "widget": [{"text": "Barthez est le meilleur <mask> du monde."}], "pipeline_tag": "fill-mask"} | moussaKam/barthez | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"bart",
"fill-mask",
"fr",
"arxiv:2010.12321",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12321"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #bart #fill-mask #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| A french sequence to sequence pretrained model based on BART.
BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining.
Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez i... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #bart #fill-mask #fr #arxiv-2010.12321 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_medium_bert-base_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_medium_bert-base_mover-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_medium_deberta_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_medium_roberta_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_small_bert-base_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_small_bert-base_mover-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_small_deberta_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_small_roberta_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_tiny_bert-base_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #has_space #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_tiny_bert-base_mover-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | {} | moussaKam/frugalscore_tiny_deberta_bert-score | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08559"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us
| FrugalScore
===========
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: URL
Project github: URL
The pretrained checkpoints presented in the paper :
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2110.08559 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.