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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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-large-xls-r-300m-Urdu
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggi... | {"language": ["ur"], "license": "apache-2.0", "library_name": "transformers", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition", "base_model": "Harveenchadh... | kingabzpro/wav2vec2-urdu | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"ur",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:Harveenchadha/vakyansh-wav2vec2-urdu-urm-60",
"license:apache-2.0",
"model-index",
"endpoints_co... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #ur #dataset-mozilla-foundation/common_voice_8_0 #base_model-Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-Urdu
==============================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Wer: 0.5747
* Cer: 0.3268
Model description
-----------------
The training and ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #ur #dataset-mozilla-foundation/common_voice_8_0 #base_model-Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training ... |
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. -->
# distilbert-magazine-classifier
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "model-index": [{"name": "distilbert-magazine-classifier", "results": []}]} | kingla6/distilbert-magazine-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-magazine-classifier
==============================
This model is a fine-tuned version of distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8377
* Precision: 0.25
* Recall: 0.125
* Fscore: 0.1667
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
# POS tagger based on SlovakBERT
This is a POS tagger based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model uses [Universal POS tagset (UPOS)](https://universaldependencies.org/u/pos/). The model was fine-tuned using Slovak part of [Universal Dependencies dataset](https://universaldependencies... | {"language": ["sk"], "license": "cc", "tags": ["pos"], "datasets": ["universal_dependencies"], "metrics": ["accuracy"], "widget": [{"text": "Kde t\u00e1 \u013eudsk\u00e1 du\u0161a drieme?"}]} | kinit/slovakbert-pos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"pos",
"sk",
"dataset:universal_dependencies",
"arxiv:2109.15254",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.15254"
] | [
"sk"
] | TAGS
#transformers #pytorch #roberta #token-classification #pos #sk #dataset-universal_dependencies #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
# POS tagger based on SlovakBERT
This is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS). The model was fine-tuned using Slovak part of Universal Dependencies dataset [Zeman 2017] containing 10k manually annotated Slovak sentences.
## Results
The model was evaluated in our paper [Pikul... | [
"# POS tagger based on SlovakBERT\n\nThis is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS). The model was fine-tuned using Slovak part of Universal Dependencies dataset [Zeman 2017] containing 10k manually annotated Slovak sentences.",
"## Results\n\nThe model was evaluated in our p... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #pos #sk #dataset-universal_dependencies #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"# POS tagger based on SlovakBERT\n\nThis is a POS tagger based on SlovakBERT. The model uses Universal POS tagset (UPOS).... |
text-classification | transformers |
# Sentiment Analysis model based on SlovakBERT
This is a sentiment analysis classifier based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model can distinguish three level of sentiment:
- `-1` - Negative sentiment
- `0` - Neutral sentiment
- `1` - Positive setiment
The model was fine-tuned usin... | {"language": ["sk"], "license": "cc", "tags": ["twitter", "sentiment-analysis"], "metrics": ["f1"], "widget": [{"text": "Najkraj\u0161ia viano\u010dn\u00e1 reklama: Toto mil\u00e9 video v\u00e1m vyk\u00fazli \u010darovn\u00fa atmosf\u00e9ru: Vianoce sa nezadr\u017eate\u013ene bl\u00ed\u017eia."}, {"text": "A op\u00e4\u... | kinit/slovakbert-sentiment-twitter | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"twitter",
"sentiment-analysis",
"sk",
"arxiv:2109.15254",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.15254"
] | [
"sk"
] | TAGS
#transformers #pytorch #roberta #text-classification #twitter #sentiment-analysis #sk #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us
|
# Sentiment Analysis model based on SlovakBERT
This is a sentiment analysis classifier based on SlovakBERT. The model can distinguish three level of sentiment:
- '-1' - Negative sentiment
- '0' - Neutral sentiment
- '1' - Positive setiment
The model was fine-tuned using Slovak part of Multilingual Twitter Sentimen... | [
"# Sentiment Analysis model based on SlovakBERT\n\nThis is a sentiment analysis classifier based on SlovakBERT. The model can distinguish three level of sentiment:\n\n- '-1' - Negative sentiment\n- '0' - Neutral sentiment\n- '1' - Positive setiment\n\nThe model was fine-tuned using Slovak part of Multilingual Twitt... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #twitter #sentiment-analysis #sk #arxiv-2109.15254 #license-cc #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment Analysis model based on SlovakBERT\n\nThis is a sentiment analysis classifier based on SlovakBERT. The model can disti... |
sentence-similarity | sentence-transformers |
# Sentence similarity model based on SlovakBERT
This is a sentence similarity model based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model was fine-tuned using [STSbenchmark](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) [Cer et al 2017] translated to Slovak using [M2M100](https://hug... | {"language": ["sk"], "license": "cc", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "sts"], "datasets": ["glue"], "metrics": ["spearmanr"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "Izrael uskuto\u010dnil leteck\u00e9 \u00fadery v bl\u00edzkosti Damasku.", "... | kinit/slovakbert-sts-stsb | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"sts",
"sk",
"dataset:glue",
"arxiv:2109.15254",
"license:cc",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.15254"
] | [
"sk"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #sts #sk #dataset-glue #arxiv-2109.15254 #license-cc #endpoints_compatible #has_space #region-us
|
# Sentence similarity model based on SlovakBERT
This is a sentence similarity model based on SlovakBERT. The model was fine-tuned using STSbenchmark [Cer et al 2017] translated to Slovak using M2M100. The model can be used as an universal sentence encoder for Slovak sentences.
## Results
The model was evaluated in... | [
"# Sentence similarity model based on SlovakBERT\n\nThis is a sentence similarity model based on SlovakBERT. The model was fine-tuned using STSbenchmark [Cer et al 2017] translated to Slovak using M2M100. The model can be used as an universal sentence encoder for Slovak sentences.",
"## Results\n\nThe model was e... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #sts #sk #dataset-glue #arxiv-2109.15254 #license-cc #endpoints_compatible #has_space #region-us \n",
"# Sentence similarity model based on SlovakBERT\n\nThis is a sentence similarity model based on SlovakBERT. The model was ... |
text-generation | transformers |
#RickSanChez | {"tags": ["conversational"]} | kipiiler/Rickbot | 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
|
#RickSanChez | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | ## Model Description
This model is based off **Sentence-Transformer's** `distiluse-base-multilingual-cased` multilingual model that has been extended to understand sentence embeddings in Estonian.
## Sentence-Transformers
This model can be imported directly via the SentenceTransformers package as shown below:
```py... | {"language": "et"} | kiri-ai/distiluse-base-multilingual-cased-et | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"et",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #distilbert #feature-extraction #et #endpoints_compatible #region-us
| ## Model Description
This model is based off Sentence-Transformer's 'distiluse-base-multilingual-cased' multilingual model that has been extended to understand sentence embeddings in Estonian.
## Sentence-Transformers
This model can be imported directly via the SentenceTransformers package as shown below:
## Fine... | [
"## Model Description\n\nThis model is based off Sentence-Transformer's 'distiluse-base-multilingual-cased' multilingual model that has been extended to understand sentence embeddings in Estonian.",
"## Sentence-Transformers\n\nThis model can be imported directly via the SentenceTransformers package as shown belo... | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #et #endpoints_compatible #region-us \n",
"## Model Description\n\nThis model is based off Sentence-Transformer's 'distiluse-base-multilingual-cased' multilingual model that has been extended to understand sentence embeddings in Estonian.",
"## Sente... |
text-generation | transformers | # Pytorch int8 quantized version of gpt2-large
## Usage
Download the .bin file locally.
Load with:
Rest of the usage according to [original instructions](https://huggingface.co/gpt2-large).
```python
import torch
model = torch.load("path/to/pytorch_model_quantized.bin")
```
| {"language": ["en"]} | kiri-ai/gpt2-large-quantized | null | [
"transformers",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Pytorch int8 quantized version of gpt2-large
## Usage
Download the .bin file locally.
Load with:
Rest of the usage according to original instructions.
| [
"# Pytorch int8 quantized version of gpt2-large",
"## Usage\n\nDownload the .bin file locally.\nLoad with:\n\nRest of the usage according to original instructions."
] | [
"TAGS\n#transformers #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pytorch int8 quantized version of gpt2-large",
"## Usage\n\nDownload the .bin file locally.\nLoad with:\n\nRest of the usage according to original instructions."
] |
text2text-generation | transformers | # T5 Base with QA + Summary + Emotion
## Dependencies
Requires transformers>=4.0.0
## Description
This model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail.
It achieves a score of **F1 79.5** on the Squad 2 dev set and a score of **F1 70.6** on the CoQa dev set.
Summarisation and emotion detecti... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering", "emotion-detection", "summarisation"], "datasets": ["coqa", "squad_v2", "go_emotions", "cnn_dailymail"], "metrics": ["f1"], "pipeline_tag": "text2text-generation", "widget": [{"text": "q: Who is Elon Musk? a: an entrepreneur q: When was he bor... | kiri-ai/t5-base-qa-summary-emotion | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-answering",
"emotion-detection",
"summarisation",
"en",
"dataset:coqa",
"dataset:squad_v2",
"dataset:go_emotions",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-gen... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-answering #emotion-detection #summarisation #en #dataset-coqa #dataset-squad_v2 #dataset-go_emotions #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # T5 Base with QA + Summary + Emotion
## Dependencies
Requires transformers>=4.0.0
## Description
This model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail.
It achieves a score of F1 79.5 on the Squad 2 dev set and a score of F1 70.6 on the CoQa dev set.
Summarisation and emotion detection has n... | [
"# T5 Base with QA + Summary + Emotion",
"## Dependencies\n\nRequires transformers>=4.0.0",
"## Description\n\nThis model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail.\n\nIt achieves a score of F1 79.5 on the Squad 2 dev set and a score of F1 70.6 on the CoQa dev set.\n\nSummarisation and em... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-answering #emotion-detection #summarisation #en #dataset-coqa #dataset-squad_v2 #dataset-go_emotions #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5 Base with Q... |
text-classification | transformers | # Reddit exercise feedback classification
Model to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,
### Usage:
```py
from transformers import pipeline
classifier = pipeline("text-classification", "kittinan/exercise-fee... | {} | kittinan/exercise-feedback-classification | 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
| # Reddit exercise feedback classification
Model to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,
### Usage:
| [
"# Reddit exercise feedback classification\n\nModel to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,",
"### Usage:"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Reddit exercise feedback classification\n\nModel to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it lo... |
null | null |
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
mak... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | kjackson/distilbert-base-uncased-finetuned-emotion | null | [
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1907.11692 #license-mit #region-us
|
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERTa did not write a mo... | [
"# RoBERTa base model\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it\nmakes a difference between english and English.\n\nDisclaimer: The team releasing RoBERTa did not... | [
"TAGS\n#exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1907.11692 #license-mit #region-us \n",
"# RoBERTa base model\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensi... |
token-classification | transformers |
# Nominalization Detector
This model identifies "predicative nominalizations", that is, nominalizations that carry an eventive (or "verbal") meaning in context. It is a `bert-base-cased` pretrained model, fine-tuned for token classification on top of the "nominalization detection" task as defined and annotated by the... | {"language": ["en"], "tags": ["pytorch", "token-classification", "nominalizations"], "datasets": ["kleinay/qanom"]} | kleinay/nominalization-candidate-classifier | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"nominalizations",
"en",
"dataset:kleinay/qanom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #nominalizations #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Nominalization Detector
This model identifies "predicative nominalizations", that is, nominalizations that carry an eventive (or "verbal") meaning in context. It is a 'bert-base-cased' pretrained model, fine-tuned for token classification on top of the "nominalization detection" task as defined and annotated by the... | [
"# Nominalization Detector\n\nThis model identifies \"predicative nominalizations\", that is, nominalizations that carry an eventive (or \"verbal\") meaning in context. It is a 'bert-base-cased' pretrained model, fine-tuned for token classification on top of the \"nominalization detection\" task as defined and anno... | [
"TAGS\n#transformers #pytorch #bert #token-classification #nominalizations #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Nominalization Detector\n\nThis model identifies \"predicative nominalizations\", that is, nominalizations that carry an eventive (or \"ve... |
text2text-generation | transformers |
# A Seq2Seq model for QANom parsing
This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](https://qasrl.org) for the nominal predicates domain. See the [... | {"language": ["en"], "tags": ["semantic-role-labeling", "question-answer generation", "pytorch"], "datasets": ["kleinay/qanom"]} | kleinay/qanom-seq2seq-model-baseline | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"semantic-role-labeling",
"question-answer generation",
"en",
"dataset:kleinay/qanom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #semantic-role-labeling #question-answer generation #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# A Seq2Seq model for QANom parsing
This is a 't5-small' pretrained model, fine-tuned on the task of generating QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which is an adaptation of QASRL (Question-Answer driven Semantic Role Labeling) for the nominal predicates domain. See the QANom paper for detail... | [
"# A Seq2Seq model for QANom parsing\n\nThis is a 't5-small' pretrained model, fine-tuned on the task of generating QANom QAs. \n\n\"QANom\" stands for \"QASRL for Nominalizations\", which is an adaptation of QASRL (Question-Answer driven Semantic Role Labeling) for the nominal predicates domain. See the QANom pape... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #semantic-role-labeling #question-answer generation #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# A Seq2Seq model for QANom parsing\n\nThis is a 't5-small' pretrained model, fi... |
text2text-generation | transformers |
# A Seq2Seq model for QANom parsing
This is a `t5-small` pretrained model, fine-tuned jointly on the tasks of generating QASRL and QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](https://qasrl.org) for the nominal predicate... | {"language": ["en"], "tags": ["semantic-role-labeling", "question-answer generation", "pytorch"], "datasets": ["kleinay/qanom"]} | kleinay/qanom-seq2seq-model-joint | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"semantic-role-labeling",
"question-answer generation",
"en",
"dataset:kleinay/qanom",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #semantic-role-labeling #question-answer generation #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# A Seq2Seq model for QANom parsing
This is a 't5-small' pretrained model, fine-tuned jointly on the tasks of generating QASRL and QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which is an adaptation of QASRL (Question-Answer driven Semantic Role Labeling) for the nominal predicates domain. See the QAN... | [
"# A Seq2Seq model for QANom parsing\n\nThis is a 't5-small' pretrained model, fine-tuned jointly on the tasks of generating QASRL and QANom QAs. \n\n\"QANom\" stands for \"QASRL for Nominalizations\", which is an adaptation of QASRL (Question-Answer driven Semantic Role Labeling) for the nominal predicates domain.... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #semantic-role-labeling #question-answer generation #en #dataset-kleinay/qanom #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# A Seq2Seq model for QANom parsing\n\nThis is a 't5-small' pretrained model, fi... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trained_model2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "trained_model2", "results": []}]} | kloon99/KML_Eula_generate_v1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# trained_model2
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followi... | [
"# trained_model2\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trainin... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# trained_model2\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.",
"## Model description\n\nMore inf... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trained_model2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "trained_model2", "results": []}]} | kloon99/KML_Eula_generate_v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# trained_model2
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followi... | [
"# trained_model2\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trainin... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# trained_model2\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.",
"## Model description\n\nMore inf... |
text-classification | transformers | {'C0': 'audit_rights',
'C1': 'licensee_indemnity',
'C2': 'licensor_indemnity',
'C3': 'license_grant',
'C4': 'eula_others',
'C5': 'licensee_infringement_indemnity',
'C6': 'licensor_exemption_liability',
'C7': 'licensor_limit_liabilty',
'C8': 'software_warranty'} | {} | kloon99/KML_Software_License_v1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| {'C0': 'audit_rights',
'C1': 'licensee_indemnity',
'C2': 'licensor_indemnity',
'C3': 'license_grant',
'C4': 'eula_others',
'C5': 'licensee_infringement_indemnity',
'C6': 'licensor_exemption_liability',
'C7': 'licensor_limit_liabilty',
'C8': 'software_warranty'} | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# KLUE BERT base
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental... | {"language": "ko", "license": "cc-by-sa-4.0", "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | klue/bert-base | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"korean",
"klue",
"ko",
"arxiv:2105.09680",
"arxiv:1910.09700",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.09680",
"1910.09700"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #korean #klue #ko #arxiv-2105.09680 #arxiv-1910.09700 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| KLUE BERT base
==============
Table of Contents
-----------------
* Model Details
* How to Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Des... | [
"#### Direct Use\n\n\nThe model can be used for tasks including topic classification, semantic textual similarity, natural language inference, named entity recognition, and other tasks outlined in the KLUE Benchmark.",
"#### Misuse and Out-of-scope Use\n\n\nThe model should not be used to intentionally create hos... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #korean #klue #ko #arxiv-2105.09680 #arxiv-1910.09700 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Direct Use\n\n\nThe model can be used for tasks including topic classification, semantic textual simi... |
fill-mask | transformers |
# KLUE RoBERTa base
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## How to use
_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
```python
from tra... | {"language": "ko", "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | klue/roberta-base | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"korean",
"klue",
"ko",
"arxiv:2105.09680",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.09680"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #korean #klue #ko #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# KLUE RoBERTa base
Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.
## How to use
_NOTE:_ Use 'BertTokenizer' instead of RobertaTokenizer. ('AutoTokenizer' will load 'BertTokenizer')
## BibTeX entry and citation info
| [
"# KLUE RoBERTa base\n\nPretrained RoBERTa Model on Korean Language. See Github and Paper for more details.",
"## How to use\n\n_NOTE:_ Use 'BertTokenizer' instead of RobertaTokenizer. ('AutoTokenizer' will load 'BertTokenizer')",
"## BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #korean #klue #ko #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# KLUE RoBERTa base\n\nPretrained RoBERTa Model on Korean Language. See Github and Paper for more details.",
"## How to use\n\n_NOTE:_ Use 'B... |
fill-mask | transformers |
# KLUE RoBERTa large
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## How to use
_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
```python
from tr... | {"language": "ko", "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | klue/roberta-large | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"korean",
"klue",
"ko",
"arxiv:2105.09680",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.09680"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #korean #klue #ko #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# KLUE RoBERTa large
Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.
## How to use
_NOTE:_ Use 'BertTokenizer' instead of RobertaTokenizer. ('AutoTokenizer' will load 'BertTokenizer')
## BibTeX entry and citation info
| [
"# KLUE RoBERTa large\n\nPretrained RoBERTa Model on Korean Language. See Github and Paper for more details.",
"## How to use\n\n_NOTE:_ Use 'BertTokenizer' instead of RobertaTokenizer. ('AutoTokenizer' will load 'BertTokenizer')",
"## BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #korean #klue #ko #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# KLUE RoBERTa large\n\nPretrained RoBERTa Model on Korean Language. See Github and Paper for more details.",
"## How to use\n\n_NOTE:_ Use '... |
fill-mask | transformers |
# KLUE RoBERTa small
Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
## How to use
_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
```python
from tr... | {"language": "ko", "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubbfc\uad6d\uc758 \uc218\ub3c4\ub294 [MASK] \uc785\ub2c8\ub2e4."}]} | klue/roberta-small | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"korean",
"klue",
"ko",
"arxiv:2105.09680",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.09680"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #korean #klue #ko #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# KLUE RoBERTa small
Pretrained RoBERTa Model on Korean Language. See Github and Paper for more details.
## How to use
_NOTE:_ Use 'BertTokenizer' instead of RobertaTokenizer. ('AutoTokenizer' will load 'BertTokenizer')
## BibTeX entry and citation info
| [
"# KLUE RoBERTa small\n\nPretrained RoBERTa Model on Korean Language. See Github and Paper for more details.",
"## How to use\n\n_NOTE:_ Use 'BertTokenizer' instead of RobertaTokenizer. ('AutoTokenizer' will load 'BertTokenizer')",
"## BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #korean #klue #ko #arxiv-2105.09680 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# KLUE RoBERTa small\n\nPretrained RoBERTa Model on Korean Language. See Github and Paper for more details.",
"## How to use\n\n_NOTE:_ Use '... |
summarization | transformers |
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer... | {"language": "en", "tags": ["summarization"]} | kmfoda/staging-pegasus-gmeetsamsum | null | [
"transformers",
"pytorch",
"pegasus",
"feature-extraction",
"summarization",
"en",
"arxiv:1912.08777",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #feature-extraction #summarization #en #arxiv-1912.08777 #endpoints_compatible #region-us
| ### Pegasus Models
See Docs: here
Original TF 1 code here
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: @sshleifer
Task: Summarization
The following is copied from the authors' README.
Mixed & Stochastic Checkpoints
==============================
We tr... | [
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @sshleifer\n\n\nTask: Summarization\n\n\nThe following is copied from the authors' README.\n\n\nMixed & Stochastic Checkpoints\n===========... | [
"TAGS\n#transformers #pytorch #pegasus #feature-extraction #summarization #en #arxiv-1912.08777 #endpoints_compatible #region-us \n",
"### Pegasus Models\n\n\nSee Docs: here\n\n\nOriginal TF 1 code here\n\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\n\nMaintained by: @ss... |
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](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can ... | {"language": "ar", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Arabic by Othmane Rifki", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spe... | kmfoda/wav2vec2-large-xlsr-arabic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ar",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"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 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as fo... | [
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the Common Voice. \nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can b... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ar #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Arabic\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic usin... |
text-generation | transformers |
#Harry Potter model | {"tags": ["conversational"]} | knightbat/harry-potter | 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 model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | transformers | Model obtained by Fine Tuning 'facebook/bart-large-xsum'
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-stor... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI"], "metrics": ["rouge"], "widget": [{"text": "Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the projec... | knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #bart #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model obtained by Fine Tuning 'facebook/bart-large-xsum'
## Usage
# Example 1
# Example 2
# Example 3
# Example 4
| [
"## Usage",
"# Example 1",
"# Example 2",
"# Example 3",
"# Example 4"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bart #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Usage",
"# Example 1",
"# Example 2",
"# Example 3",
"# Example 4"
] |
summarization | transformers | Model obtained by Fine Tuning 'facebook/bart-large-xsum'
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM")
text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey b... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["cnndaily/newyorkdaily/xsum/samsum/dialogsum"], "metrics": ["rouge"], "widget": [{"text": "Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is... | knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #bart #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model obtained by Fine Tuning 'facebook/bart-large-xsum'
## Usage
# Example 1
# Example 2
# Example 3
# Example 4
| [
"## Usage",
"# Example 1",
"# Example 2",
"# Example 3",
"# Example 4"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bart #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Usage",
"# Example 1",
"# Example 2",
"# Example 3",
"# Example 4"
] |
summarization | transformers | Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
text = '''The tower is 324 metres (1,063 ft) tal... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI"], "metrics": ["rouge"], "widget": [{"text": "Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the projec... | knkarthick/MEETING_SUMMARY | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #bart #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!
## Usage
# Example 1
# Example 2
# Example 3
# Example 4
| [
"## Usage",
"# Example 1",
"# Example 2",
"# Example 3",
"# Example 4"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bart #text2text-generation #seq2seq #summarization #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Usage",
"# Example 1",
"# Example 2",
"# Example 3",
"# Example 4"
] |
summarization | transformers | ## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/bart-large-xsum-samsum")
conversation = '''Hannah: Hey,... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry, can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at the park together\nHannah: I ... | knkarthick/bart-large-xsum-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| ## 'bart-large-xsum-samsum'
This model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum dataset.
## Usage
| [
"## 'bart-large-xsum-samsum'\nThis model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum dataset.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## 'bart-large-xsum-samsum'\nThis model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum dataset.",
... |
summarization | transformers | ## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/bart-large-xsum-samsum")
conversation = '''Hannah: Hey,... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry, can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at the park together\nHannah: I ... | knkarthick/meeting-summary-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## 'bart-large-xsum-samsum'
This model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum dataset.
## Usage
| [
"## 'bart-large-xsum-samsum'\nThis model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum dataset.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## 'bart-large-xsum-samsum'\nThis model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum da... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | knlu1016/albert-base-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| albert-base-v2-finetuned-squad
==============================
This model is a fine-tuned version of albert-base-v2 on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1607
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_size:... |
null | null | vi_law_bert
| {} | kodiak619/vi_law_bert | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| vi_law_bert
| [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers | Testing Khmer ASR baseline. | {} | kongkeaouch/wav2vec2-xls-r-300m-kh | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Testing Khmer ASR baseline. | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_core_med7_lg` |
| **Version** | `3.4.2.1` |
| **spaCy** | `>=3.4.2,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License*... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | kormilitzin/en_core_med7_lg | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
|
### Label Scheme
View label scheme (7 labels for 1 components)
### Accuracy
### BibTeX entry and citation info
| [
"### Label Scheme\n\n\n\nView label scheme (7 labels for 1 components)",
"### Accuracy",
"### BibTeX entry and citation info"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (7 labels for 1 components)",
"### Accuracy",
"### BibTeX entry and citation info"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_core_med7_trf` |
| **Version** | `3.4.2.1` |
| **spaCy** | `>=3.4.2,<3.5.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| *... | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | kormilitzin/en_core_med7_trf | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-mit #model-index #has_space #region-us
|
### Label Scheme
View label scheme (7 labels for 1 components)
### Accuracy
### BibTeX entry and citation info
| [
"### Label Scheme\n\n\n\nView label scheme (7 labels for 1 components)",
"### Accuracy",
"### BibTeX entry and citation info"
] | [
"TAGS\n#spacy #token-classification #en #license-mit #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (7 labels for 1 components)",
"### Accuracy",
"### BibTeX entry and citation info"
] |
feature-extraction | transformers | Converted for Tensorflow
```
!pip install transformers sentencepiece
from transformers import TFAutoModel, AutoTokenizer
name = "ai4bharat/indic-bert"
model = TFAutoModel.from_pretrained(name, from_pt=True)
tokenizer = AutoTokenizer.from_pretrained(name)
model.save_pretrained("local-indic-bert")
tokenizer.save_pretrai... | {} | kornesh/indic-bert | null | [
"transformers",
"tf",
"albert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #albert #feature-extraction #endpoints_compatible #region-us
| Converted for Tensorflow
| [] | [
"TAGS\n#transformers #tf #albert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | Converted for Tensorflow
```
!pip install transformers sentencepiece
from transformers import TFAutoModel, AutoTokenizer
name = "xlm-roberta-base"
model = TFAutoModel.from_pretrained(name, from_pt=True)
tokenizer = AutoTokenizer.from_pretrained(name)
model.save_pretrained("local-xlm-roberta-base")
tokenizer.save_pretra... | {} | kornesh/xlm-roberta-base | null | [
"transformers",
"tf",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #xlm-roberta #feature-extraction #endpoints_compatible #region-us
| Converted for Tensorflow
| [] | [
"TAGS\n#transformers #tf #xlm-roberta #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | Converted for Tensorflow
```
name = "xlm-roberta-large"
!rm -rf local
!git clone https://huggingface.co/kornesh/"$name" local
model = TFAutoModel.from_pretrained(name, from_pt=True)
tokenizer = AutoTokenizer.from_pretrained(name)
model.save_pretrained("local")
tokenizer.save_pretrained("local")
!cd local/ && git lfs in... | {} | kornesh/xlm-roberta-large | null | [
"transformers",
"tf",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #xlm-roberta #feature-extraction #endpoints_compatible #region-us
| Converted for Tensorflow
| [] | [
"TAGS\n#transformers #tf #xlm-roberta #feature-extraction #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (KE-MLM)
Pre-trained weights for **KE-MLM model** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on ov... | {"language": "en", "license": "gpl-3.0", "tags": ["twitter", "stance-detection", "election2020", "politics"]} | kornosk/bert-election2020-twitter-stance-biden-KE-MLM | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (KE-MLM)
Pre-trained weights for KE-MLM model in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential E... | [
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (KE-MLM)\n\nPre-trained weights for KE-MLM model in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.",
"# Training Data\n\nThis model is pre-trained on over 5 million English tweets about the 2020 US Pr... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (KE-MLM)\n\nPre-trained weights f... |
text-classification | transformers |
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 m... | {"language": "en", "license": "gpl-3.0", "tags": ["twitter", "stance-detection", "election2020", "politics"]} | kornosk/bert-election2020-twitter-stance-biden | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)
Pre-trained weights for f-BERT in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Electio... | [
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)\n\nPre-trained weights for f-BERT in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.",
"# Training Data\n\nThis model is pre-trained on over 5 million English tweets about the 2020 US Presiden... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)\n\nPre-trained weights f... |
text-classification | transformers |
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (KE-MLM)
Pre-trained weights for **KE-MLM model** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on... | {"language": "en", "license": "gpl-3.0", "tags": ["twitter", "stance-detection", "election2020", "politics"]} | kornosk/bert-election2020-twitter-stance-trump-KE-MLM | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (KE-MLM)
Pre-trained weights for KE-MLM model in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidentia... | [
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (KE-MLM)\n\nPre-trained weights for KE-MLM model in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.",
"# Training Data\n\nThis model is pre-trained on over 5 million English tweets about the 2020 US... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (KE-MLM)\n\nPre-trained weight... |
text-classification | transformers |
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over ... | {"language": "en", "license": "gpl-3.0", "tags": ["twitter", "stance-detection", "election2020", "politics"]} | kornosk/bert-election2020-twitter-stance-trump | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)
Pre-trained weights for f-BERT in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Elec... | [
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)\n\nPre-trained weights for f-BERT in Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.",
"# Training Data\n\nThis model is pre-trained on over 5 million English tweets about the 2020 US Presi... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #twitter #stance-detection #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)\n\nPre-trained weight... |
fill-mask | transformers |
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
We use the initialized weights from BERT-base (uncased) or `bert-base-uncased`.
# Training Data
This mod... | {"language": "en", "license": "gpl-3.0", "tags": ["twitter", "masked-token-prediction", "election2020", "politics"]} | kornosk/bert-political-election2020-twitter-mlm | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"twitter",
"masked-token-prediction",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #twitter #masked-token-prediction #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.
We use the initialized weights from BERT-base (uncased) or 'bert-base-uncased'.
# Training Data
This model is pre-trained on over 5 million English tweets about... | [
"# Pre-trained BERT on Twitter US Political Election 2020\n\nPre-trained weights for Knowledge Enhance Masked Language Model for Stance Detection, NAACL 2021.\n\nWe use the initialized weights from BERT-base (uncased) or 'bert-base-uncased'.",
"# Training Data\n\nThis model is pre-trained on over 5 million Englis... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #twitter #masked-token-prediction #election2020 #politics #en #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained BERT on Twitter US Political Election 2020\n\nPre-trained weights for Knowledge Enhance Masked Language Mode... |
feature-extraction | transformers | hello
| {} | kouohhashi/roberta_ja | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #feature-extraction #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #feature-extraction #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # Tony Stark DialoGPT Model | {"tags": ["Conversational"]} | kp17/DialoGPT-small-tonystark | 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
| # Tony Stark DialoGPT Model | [
"# Tony Stark DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #Conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tony Stark DialoGPT Model"
] |
null | null | # SaShiMi
> **It's Raw! Audio Generation with State-Space Models**\
> Karan Goel, Albert Gu, Chris Donahue, Christopher Ré\
> Paper: https://arxiv.org/pdf/2202.09729.pdf
This repository contains a release of the artifacts for the SaShiMi paper. To use our code and... | {} | krandiash/sashimi-release | null | [
"arxiv:2202.09729",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.09729"
] | [] | TAGS
#arxiv-2202.09729 #region-us
| # SaShiMi
!SaShiMi
> It's Raw! Audio Generation with State-Space Models\
> Karan Goel, Albert Gu, Chris Donahue, Christopher Ré\
> Paper: URL
This repository contains a release of the artifacts for the SaShiMi paper. To use our code and artifacts in your research, please refer to the instructions at URL | [
"# SaShiMi\n\n!SaShiMi\n> It's Raw! Audio Generation with State-Space Models\\\n> Karan Goel, Albert Gu, Chris Donahue, Christopher Ré\\\n> Paper: URL\n\nThis repository contains a release of the artifacts for the SaShiMi paper. To use our code and artifacts in your research, please refer to the instructions at URL... | [
"TAGS\n#arxiv-2202.09729 #region-us \n",
"# SaShiMi\n\n!SaShiMi\n> It's Raw! Audio Generation with State-Space Models\\\n> Karan Goel, Albert Gu, Chris Donahue, Christopher Ré\\\n> Paper: URL\n\nThis repository contains a release of the artifacts for the SaShiMi paper. To use our code and artifacts in your resear... |
automatic-speech-recognition | transformers |
## Evaluation on Zeroth-Korean ASR corpus
[Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb)
```
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import soundfile as sf
import torch
from ... | {"language": "ko", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["kresnik/zeroth_korean"], "model-index": [{"name": "Wav2Vec2 XLSR Korean", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Zer... | kresnik/wav2vec2-large-xlsr-korean | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"ko",
"dataset:kresnik/zeroth_korean",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech #audio #ko #dataset-kresnik/zeroth_korean #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
## Evaluation on Zeroth-Korean ASR corpus
Google colab notebook(Korean)
### Expected WER: 4.74%
### Expected CER: 1.78% | [
"## Evaluation on Zeroth-Korean ASR corpus\n\nGoogle colab notebook(Korean)",
"### Expected WER: 4.74%",
"### Expected CER: 1.78%"
] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech #audio #ko #dataset-kresnik/zeroth_korean #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"## Evaluation on Zeroth-Korean ASR corpus\n\nGoogle colab notebook(Korean)",
"### Expected WER: ... |
null | transformers |
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-discriminator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
>... | {"language": "ko"} | krevas/finance-koelectra-base-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
|
# Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean ('finance-koelectra-base-discriminator')
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> d... | [
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-base-discriminator')\n\n> ELECTRA is a new method for self-supervised language representation learning. It can be used to\n> pre-train transformer networks using relatively little compute. ELECTRA models are train... | [
"TAGS\n#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us \n",
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-base-discriminator')\n\n> ELECTRA is a new method for self-supervised language representation learning. It can be use... |
fill-mask | transformers |
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-generator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> dis... | {"language": "ko"} | krevas/finance-koelectra-base-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
|
# Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean ('finance-koelectra-base-generator')
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> disti... | [
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-base-generator')\n\n> ELECTRA is a new method for self-supervised language representation learning. It can be used to\n> pre-train transformer networks using relatively little compute. ELECTRA models are trained t... | [
"TAGS\n#transformers #pytorch #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-base-generator')\n\n> ELECTRA is a new method for self-supervised language representation learnin... |
null | transformers |
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-discriminator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
... | {"language": "ko"} | krevas/finance-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
|
# Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean ('finance-koelectra-small-discriminator')
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> ... | [
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-small-discriminator')\n\n> ELECTRA is a new method for self-supervised language representation learning. It can be used to\n> pre-train transformer networks using relatively little compute. ELECTRA models are trai... | [
"TAGS\n#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us \n",
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-small-discriminator')\n\n> ELECTRA is a new method for self-supervised language representation learning. It can be us... |
fill-mask | transformers |
# 📈 Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean (`finance-koelectra-small-generator`)
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> di... | {"language": "ko"} | krevas/finance-koelectra-small-generator | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us
|
# Financial Korean ELECTRA model
Pretrained ELECTRA Language Model for Korean ('finance-koelectra-small-generator')
> ELECTRA is a new method for self-supervised language representation learning. It can be used to
> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
> dist... | [
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-small-generator')\n\n> ELECTRA is a new method for self-supervised language representation learning. It can be used to\n> pre-train transformer networks using relatively little compute. ELECTRA models are trained ... | [
"TAGS\n#transformers #pytorch #safetensors #electra #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# Financial Korean ELECTRA model\n\nPretrained ELECTRA Language Model for Korean ('finance-koelectra-small-generator')\n\n> ELECTRA is a new method for self-supervised language represen... |
text-generation | transformers |
# Phoenix DialoGPT model | {"tags": ["conversational"]} | kripanshudixit/DialoGPT-small-phoenix | 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
|
# Phoenix DialoGPT model | [
"# Phoenix DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Phoenix DialoGPT model"
] |
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-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | krirk/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"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 #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3942
* Wer: 0.3149
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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* t... |
text-generation | transformers |
#Spock Model | {"tags": ["conversational"]} | kris/DialoGPT-small-spock | null | [
"transformers",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Spock Model | [] | [
"TAGS\n#transformers #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Spock model | {"tags": ["conversational"]} | kris/DialoGPT-small-spock3 | 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
|
#Spock model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Spock model | {"tags": ["conversational"]} | kris/DialoGPT-small-spock4 | 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
|
#Spock model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Spock model | {"tags": ["conversational"]} | kris/DialoGPT-small-spock5 | 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
|
#Spock model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# sts-GBERT-bi-encoder
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes e... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | krlng/sts-GBERT-bi-encoder | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# sts-GBERT-bi-encoder
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then ... | [
"# sts-GBERT-bi-encoder\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# sts-GBERT-bi-encoder\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like cluster... |
text-generation | transformers |
#testing bot Model | {"tags": ["conversational"]} | kshitiz/testing-bot-repo | 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
|
#testing bot Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# name
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
#... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model_index": [{"name": "name", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}}]}]} | ksmcg/name | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"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-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# name
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hy... | [
"# name\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyp... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# name\n\nThis model is a fine-tuned version of bert-base-uncased on the glue dataset.",
"## Model description\n\nMore info... |
fill-mask | transformers | # I-BERT base model
This model, `ibert-roberta-base`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this paper](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only... | {} | kssteven/ibert-roberta-base | null | [
"transformers",
"pytorch",
"ibert",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2101.01321",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2101.01321"
] | [] | TAGS
#transformers #pytorch #ibert #fill-mask #arxiv-1907.11692 #arxiv-2101.01321 #autotrain_compatible #endpoints_compatible #region-us
| # I-BERT base model
This model, 'ibert-roberta-base', is an integer-only quantized version of RoBERTa, and was introduced in this paper.
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.
In particular, I-BERT replaces all floating point operation... | [
"# I-BERT base model\n\nThis model, 'ibert-roberta-base', is an integer-only quantized version of RoBERTa, and was introduced in this paper.\nI-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.\nIn particular, I-BERT replaces all floating point ... | [
"TAGS\n#transformers #pytorch #ibert #fill-mask #arxiv-1907.11692 #arxiv-2101.01321 #autotrain_compatible #endpoints_compatible #region-us \n",
"# I-BERT base model\n\nThis model, 'ibert-roberta-base', is an integer-only quantized version of RoBERTa, and was introduced in this paper.\nI-BERT stores all parameters... |
fill-mask | transformers | # I-BERT large model
This model, `ibert-roberta-large`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this papaer](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-o... | {} | kssteven/ibert-roberta-large | null | [
"transformers",
"pytorch",
"ibert",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2101.01321",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2101.01321"
] | [] | TAGS
#transformers #pytorch #ibert #fill-mask #arxiv-1907.11692 #arxiv-2101.01321 #autotrain_compatible #endpoints_compatible #region-us
| # I-BERT large model
This model, 'ibert-roberta-large', is an integer-only quantized version of RoBERTa, and was introduced in this papaer.
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.
In particular, I-BERT replaces all floating point operat... | [
"# I-BERT large model\n\nThis model, 'ibert-roberta-large', is an integer-only quantized version of RoBERTa, and was introduced in this papaer.\nI-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.\nIn particular, I-BERT replaces all floating poi... | [
"TAGS\n#transformers #pytorch #ibert #fill-mask #arxiv-1907.11692 #arxiv-2101.01321 #autotrain_compatible #endpoints_compatible #region-us \n",
"# I-BERT large model\n\nThis model, 'ibert-roberta-large', is an integer-only quantized version of RoBERTa, and was introduced in this papaer.\nI-BERT stores all paramet... |
null | null | I love this class | {} | ktalley524/Class_Eval_Results | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| I love this class | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | # GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained... | {"language": ["en"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "the Pile", "causal-lm"], "datasets": ["the Pile"]} | ktangri/gpt-neo-demo | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"the Pile",
"causal-lm",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #the Pile #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| GPT-Neo 2.7B (By EleutherAI)
============================
Model Description
-----------------
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained mo... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:",
"### Limitations and Biases\n\n\nGPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text an... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #text generation #the Pile #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. This example generates a different ... |
question-answering | transformers | ### Model
**[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Training Parameters
Trained on 4 NVIDI... | {} | ktrapeznikov/albert-xlarge-v2-squad-v2 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #has_space #region-us
| ### Model
'albert-xlarge-v2' fine-tuned on 'SQuAD V2' using 'run\_squad.py'
### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
### Evaluation
Evaluation on the dev set. I did not sweep for best threshold.
### Usage
See huggingface documentation. Training on 'SQuAD V2' allows the model t... | [
"### Model\n\n\n'albert-xlarge-v2' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluation on the dev set. I did not sweep for best threshold.",
"### Usage\n\n\nSee huggingface documentation. Training on 'SQ... | [
"TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #has_space #region-us \n",
"### Model\n\n\n'albert-xlarge-v2' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluation on the de... |
question-answering | transformers | ### Model
**[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
This model is case... | {} | ktrapeznikov/biobert_v1.1_pubmed_squad_v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us
| ### Model
'monologg/biobert\_v1.1\_pubmed' fine-tuned on 'SQuAD V2' using 'run\_squad.py'
This model is cased.
### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
### Evaluation
Evaluation on the dev set. I did not sweep for best threshold.
### Usage
See huggingface documentation. Trai... | [
"### Model\n\n\n'monologg/biobert\\_v1.1\\_pubmed' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'\n\n\nThis model is cased.",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluation on the dev set. I did not sweep for best threshold.",
"### Usage\n\n\nSee ... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us \n",
"### Model\n\n\n'monologg/biobert\\_v1.1\\_pubmed' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'\n\n\nThis model is cased.",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Ev... |
text-generation | transformers |
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with:
```python
f"topic {topic} so... | {"language": ["en"], "widget": [{"text": "topic climate source washington post title "}]} | ktrapeznikov/gpt2-medium-topic-news-v2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"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 #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with:
Try the following tags for... | [
"# GPT2-medium-topic-news",
"## Model description\n\nGPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title",
"## Intended uses & limitations",
"#### How to use\n\nTo generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: \n\n\nTry... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-medium-topic-news",
"## Model description\n\nGPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title",
"## Intended uses & ... |
text-generation | transformers |
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a large news corpus conditioned on a topic
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, prompt model with:
`topic: climate article:`
The following tags were used during training:
`ar... | {"language": ["en"], "widget": [{"text": "topic: climate article:"}]} | ktrapeznikov/gpt2-medium-topic-news | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"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 #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a large news corpus conditioned on a topic
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, prompt model with:
'topic: climate article:'
The following tags were used during training:
'ar... | [
"# GPT2-medium-topic-news",
"## Model description\n\nGPT2-medium fine tuned on a large news corpus conditioned on a topic",
"## Intended uses & limitations",
"#### How to use\n\nTo generate a news article text conditioned on a topic, prompt model with: \n'topic: climate article:'\n\nThe following tags were us... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-medium-topic-news",
"## Model description\n\nGPT2-medium fine tuned on a large news corpus conditioned on a topic",
"## Intended uses & limitations",
"... |
text-generation | transformers |
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a small news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with:
```python
f"topic {topic} sour... | {"language": ["en"], "widget": [{"text": "topic climate source"}]} | ktrapeznikov/gpt2-medium-topic-small-set | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"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 #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a small news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with:
Try the following tags for '... | [
"# GPT2-medium-topic-news",
"## Model description\n\nGPT2-medium fine tuned on a small news corpus conditioned on a topic, source, title",
"## Intended uses & limitations",
"#### How to use\n\nTo generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: \n\n\nTry t... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2-medium-topic-news",
"## Model description\n\nGPT2-medium fine tuned on a small news corpus conditioned on a topic, source, title",
"## Intended uses & li... |
question-answering | transformers | ### Model
**[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Traini... | {} | ktrapeznikov/scibert_scivocab_uncased_squad_v2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us
| ### Model
'allenai/scibert\_scivocab\_uncased' fine-tuned on 'SQuAD V2' using 'run\_squad.py'
### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
### Evaluation
Evaluation on the dev set. I did not sweep for best threshold.
### Usage
See huggingface documentation. Training on 'SQuAD V2' ... | [
"### Model\n\n\n'allenai/scibert\\_scivocab\\_uncased' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluation on the dev set. I did not sweep for best threshold.",
"### Usage\n\n\nSee huggingface documentat... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #region-us \n",
"### Model\n\n\n'allenai/scibert\\_scivocab\\_uncased' fine-tuned on 'SQuAD V2' using 'run\\_squad.py'",
"### Training Parameters\n\n\nTrained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb",
"### Evaluation\n\n\nEvaluati... |
null | null | textsummarizer | {} | kumaran/textsummarizer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| textsummarizer | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | #House BOT | {"tags": ["conversational"]} | kunalbhargava/DialoGPT-small-housebot | 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
| #House BOT | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers | # telugu_bertu
## Model description
This model is a BERT MLM model trained on Telugu. Please use it from the terminal as the web interface has encoding issues.
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models. And also, plea... | {"language": "te"} | kuppuluri/telugu_bertu | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"te",
"doi:10.57967/hf/0264",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"te"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #te #doi-10.57967/hf/0264 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # telugu_bertu
## Model description
This model is a BERT MLM model trained on Telugu. Please use it from the terminal as the web interface has encoding issues.
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models. And also, plea... | [
"# telugu_bertu",
"## Model description\n\nThis model is a BERT MLM model trained on Telugu. Please use it from the terminal as the web interface has encoding issues.\n\nPS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models. A... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #te #doi-10.57967/hf/0264 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# telugu_bertu",
"## Model description\n\nThis model is a BERT MLM model trained on Telugu. Please use it from the terminal as the web interface has ... |
token-classification | transformers | # Named Entity Recognition Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models... | {} | kuppuluri/telugu_bertu_ner | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Named Entity Recognition Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models... | [
"# Named Entity Recognition Model for Telugu",
"#### How to use\nUse the below script from your python terminal as the web interface for inference has few encoding issues for Telugu\n\nPS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also ad... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Named Entity Recognition Model for Telugu",
"#### How to use\nUse the below script from your python terminal as the web interface for inference has few encoding issues for Tel... |
token-classification | transformers | # Part of Speech tagging Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models.
... | {} | kuppuluri/telugu_bertu_pos | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Part of Speech tagging Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models.
... | [
"# Part of Speech tagging Model for Telugu",
"#### How to use\nUse the below script from your python terminal as the web interface for inference has few encoding issues for Telugu\n\nPS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add ... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Part of Speech tagging Model for Telugu",
"#### How to use\nUse the below script from your python terminal as the web interface for inference has few encoding issues for Telug... |
question-answering | transformers | # Telugu Question-Answering model trained on Tydiqa dataset from Google
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
```python
from transformers.pipelines import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
model = AutoMo... | {} | kuppuluri/telugu_bertu_tydiqa | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #has_space #region-us
| # Telugu Question-Answering model trained on Tydiqa dataset from Google
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
## Training data
I used Tydiqa Telugu data from Google URL
PS: If you find my model useful, I would appreciate ... | [
"# Telugu Question-Answering model trained on Tydiqa dataset from Google",
"#### How to use\nUse the below script from your python terminal as the web interface for inference has few encoding issues for Telugu",
"## Training data\nI used Tydiqa Telugu data from Google URL\n\nPS: If you find my model useful, I w... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #has_space #region-us \n",
"# Telugu Question-Answering model trained on Tydiqa dataset from Google",
"#### How to use\nUse the below script from your python terminal as the web interface for inference has few encodi... |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | kurianbenoy/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"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 #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0611
* Precision: 0.9305
* Recall: 0.9505
* F1: 0.9404
* Accuracy: 0.9861
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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* learning... |
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_... | kurianbenoy/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"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 #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3073
* Accuracy: 0.923
Model description
-----------------
More information needed
... | [
"### 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 #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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. -->
# distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased-fi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type":... | kurianbenoy/distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"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 #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb
==============================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2165
* Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | null | This model can predict which categories a specific competitive problem falls into | {} | kurone/cp_tags_prediction | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This model can predict which categories a specific competitive problem falls into | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Rick DiabloGPT Model | {"tags": ["conversational"]} | kvothe28/DiabloGPT-small-Rick | 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
|
# Rick DiabloGPT Model | [
"# Rick DiabloGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DiabloGPT Model"
] |
automatic-speech-recognition | transformers | https://huggingface.co/blog/fine-tune-wav2vec2-english
Use the processor from https://huggingface.co/facebook/wav2vec2-base | {} | kwang1993/wav2vec2-base-timit-demo | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| URL
Use the processor from URL | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training p... | {} | kwang2049/TSDAE-askubuntu | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training procedure of this model:
1. Initiali... | [
"# kwang2049/TSDAE-askubuntu2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was only trained with the TSDAE objective on AskUbuntu in an unsupervised manner. Training procedure of this model:\n 1.... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-askubuntu2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was on... |
feature-extraction | transformers | # kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain AskUbuntu. Training ... | {} | kwang2049/TSDAE-askubuntu2nli_stsb | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-askubuntu2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model adapts the knowledge from the NLI and STSb data to the specific domain AskUbuntu. Training procedure of this model:
1. Initial... | [
"# kwang2049/TSDAE-askubuntu2nli_stsb\n\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adapts the knowledge from the NLI and STSb data to the specific domain AskUbuntu. Training procedure of this model:\n ... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-askubuntu2nli_stsb\n\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adap... |
feature-extraction | transformers | # kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Traini... | {} | kwang2049/TSDAE-cqadupstack | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Training procedure of this model:
1. Init... | [
"# kwang2049/TSDAE-cqadupstack2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was only trained with the TSDAE objective on cqadupstack in an unsupervised manner. Training procedure of this model:\... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-cqadupstack2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was ... |
feature-extraction | transformers | # kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain cqadupstack. Traini... | {} | kwang2049/TSDAE-cqadupstack2nli_stsb | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-cqadupstack2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model adapts the knowledge from the NLI and STSb data to the specific domain cqadupstack. Training procedure of this model:
1. Init... | [
"# kwang2049/TSDAE-cqadupstack2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adapts the knowledge from the NLI and STSb data to the specific domain cqadupstack. Training procedure of this model:\... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-cqadupstack2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adap... |
feature-extraction | transformers | # kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training proce... | {} | kwang2049/TSDAE-scidocs | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training procedure of this model:
1. Initialized ... | [
"# kwang2049/TSDAE-scidocs2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was only trained with the TSDAE objective on scidocs in an unsupervised manner. Training procedure of this model:\n 1. Ini... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-scidocs2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was only... |
feature-extraction | transformers | # kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training proce... | {} | kwang2049/TSDAE-scidocs2nli_stsb | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-scidocs2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training procedure of this model:
1. Initialized ... | [
"# kwang2049/TSDAE-scidocs2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adapts the knowledge from the NLI and STSb data to the specific domain scidocs. Training procedure of this model:\n 1. Ini... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-scidocs2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adapts t... |
feature-extraction | transformers | # kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Traini... | {} | kwang2049/TSDAE-twitterpara | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Training procedure of this model:
1. Init... | [
"# kwang2049/TSDAE-twitterpara2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was only trained with the TSDAE objective on twitterpara in an unsupervised manner. Training procedure of this model:\... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-twitterpara2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model was ... |
feature-extraction | transformers | # kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper ["TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning"](https://arxiv.org/abs/2104.06979). This model adapts the knowledge from the NLI and STSb data to the specific domain twitterpara. Traini... | {} | kwang2049/TSDAE-twitterpara2nli_stsb | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.06979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06979"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us
| # kwang2049/TSDAE-twitterpara2nli_stsb
This is a model from the paper "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning". This model adapts the knowledge from the NLI and STSb data to the specific domain twitterpara. Training procedure of this model:
1. Init... | [
"# kwang2049/TSDAE-twitterpara2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adapts the knowledge from the NLI and STSb data to the specific domain twitterpara. Training procedure of this model:\... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.06979 #endpoints_compatible #region-us \n",
"# kwang2049/TSDAE-twitterpara2nli_stsb\nThis is a model from the paper \"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning\". This model adap... |
fill-mask | transformers |
# Albert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, AlbertModel
tokenizer_albert = BertTokenizerFa... | {"language": "ko"} | kykim/albert-kor-base | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #ko #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Albert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Albert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #ko #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Albert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] |
fill-mask | transformers |
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, BertModel
tokenizer_bert = BertTokenizerFast.fro... | {"language": "ko"} | kykim/bert-kor-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Bert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Bert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] |
text2text-generation | transformers |
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
# only for pytorch in transformers
from transformers import BertTokenizerFast, EncoderDecoder... | {"language": "ko"} | kykim/bertshared-kor-base | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Bert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Bert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in... |
null | transformers |
# Electra base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import ElectraTokenizerFast, ElectraModel
tokenizer_electra = ElectraTo... | {"language": "ko"} | kykim/electra-kor-base | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #ko #endpoints_compatible #has_space #region-us
|
# Electra base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Electra base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #ko #endpoints_compatible #has_space #region-us \n",
"# Electra base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] |
feature-extraction | transformers |
# Funnel-transformer base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokeni... | {"language": "ko"} | kykim/funnel-kor-base | null | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"ko",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #funnel #feature-extraction #ko #endpoints_compatible #has_space #region-us
|
# Funnel-transformer base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Funnel-transformer base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #tf #funnel #feature-extraction #ko #endpoints_compatible #has_space #region-us \n",
"# Funnel-transformer base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] |
text-generation | transformers |
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, GPT2LMHeadModel
tokenizer_gpt3 = BertTokenizerFas... | {"language": "ko", "tags": ["text-generation"]} | kykim/gpt3-kor-small_based_on_gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in github
| [
"# Bert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language models for Korean in github"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #ko #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Bert base model for Korean\n\n* 70GB Korean text dataset and 42000 lower-cased subwords are used\n* Check the model performance and other language ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | kyo/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4718
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text2text-generation | transformers |
Google's mt5-base fine-tuned in Japanese to solve error detection and correction task.
# 日本語誤り訂正
- "吾輩をは猫である。名前えはまだない。"→"吾輩は猫である。名前はまだない。"
- "-small" has been trained on 20,000 text pairs only.
- dataset: [link](http://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9EWikipedia%E5%85%A5%E5%8A%9B%E8%AA%A4%E3%82%8A%... | {"language": "ja", "license": "mit", "widget": [{"text": "\u543e\u8f29\u3092\u306f\u732b\u3067\u3042\u308b\u3002\u3092\u66f8\u3044\u305f\u4f5c\u5bb6\u306f\uff0c\u590f\u76ee\u6f31 <extra_id_0>"}, {"text": "\u543e\u8f29\u3092\u306f\u732b\u3067\u3042\u308b\u3002\u540d\u524d\u3048\u306f\u307e\u3060\u306a\u3044\u3002"}, {"t... | kz/mt5base-finetuned-ECC-japanese-small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"ja",
"arxiv:2201.11903",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.11903"
] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #ja #arxiv-2201.11903 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Google's mt5-base fine-tuned in Japanese to solve error detection and correction task.
# 日本語誤り訂正
- "吾輩をは猫である。名前えはまだない。"→"吾輩は猫である。名前はまだない。"
- "-small" has been trained on 20,000 text pairs only.
- dataset: link *used only first 20,000 text pairs.
- prefix: "correction: " （notice: single task trained.)
- text-to-textの... | [
"# 日本語誤り訂正\n\n- \"吾輩をは猫である。名前えはまだない。\"→\"吾輩は猫である。名前はまだない。\"\n- \"-small\" has been trained on 20,000 text pairs only.\n- dataset: link *used only first 20,000 text pairs.\n- prefix: \"correction: \" （notice: single task trained.)\n- text-to-textのお気持ち体験版ぐらいの感覚でどうぞ．",
"## 参考\n\n- \"東北大学でMASKが研究をしています。\"→\"東北大学でMASK... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #ja #arxiv-2201.11903 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 日本語誤り訂正\n\n- \"吾輩をは猫である。名前えはまだない。\"→\"吾輩は猫である。名前はまだない。\"\n- \"-small\" has been trained on 20,000 text pairs only.\n- dataset: link ... |
text2text-generation | transformers |
Google's mt5-base fine-tuned in Japanese to summarize patent claims in a limited Pharmaceutical domain.
# 日本語特許請求項要約（医薬特定ドメイン限定）
- """【請求項１】
ヒトＣＤ３８（配列番号１）及びカニクイザルＣＤ３８（配列番号２）に特異的に結合する単離された抗体であって、
ａ）以下を含む重鎖可変領域：
ｉ）配列番号３を含む第１のＣＤＲ；
ｉｉ）配列番号４を含む第２のＣＤＲ；
ｉｉｉ）配列番号５を含む第３のＣＤＲ；及び
ｂ）以下を含む軽鎖可変領域：
ｉ）配列番号６を含む第１のＣＤＲ；
ｉｉ）... | {"language": "ja", "license": "mit", "tags": ["Summarization", "japanese"], "widget": [{"text": "\u8acb\u6c42\u9805 <extra_id_0>"}]} | kz/mt5base-finetuned-patentsum-japanese-small | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"Summarization",
"japanese",
"ja",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #Summarization #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Google's mt5-base fine-tuned in Japanese to summarize patent claims in a limited Pharmaceutical domain.
# 日本語特許請求項要約（医薬特定ドメイン限定）
- """【請求項１】
ヒトＣＤ３８（配列番号１）及びカニクイザルＣＤ３８（配列番号２）に特異的に結合する単離された抗体であって、
ａ）以下を含む重鎖可変領域：
ｉ）配列番号３を含む第１のＣＤＲ；
ｉｉ）配列番号４を含む第２のＣＤＲ；
ｉｉｉ）配列番号５を含む第３のＣＤＲ；及び
ｂ）以下を含む軽鎖可変領域：
ｉ）配列番号６を含む第１のＣＤＲ；
ｉｉ）... | [
"# 日本語特許請求項要約（医薬特定ドメイン限定）\n\n- \"\"\"【請求項１】\n ヒトＣＤ３８（配列番号１）及びカニクイザルＣＤ３８（配列番号２）に特異的に結合する単離された抗体であって、\nａ）以下を含む重鎖可変領域：\n ｉ）配列番号３を含む第１のＣＤＲ；\n ｉｉ）配列番号４を含む第２のＣＤＲ；\n ｉｉｉ）配列番号５を含む第３のＣＤＲ；及び\nｂ）以下を含む軽鎖可変領域：\n ｉ）配列番号６を含む第１のＣＤＲ；\n ｉｉ）配列番号７を含む第２のＣＤＲ；\n ｉｉｉ）配列番号８を含む第３のＣＤＲ；\nを含む、抗体。(請求項２～１９省略)【請求項２０】\n 前記自己免疫疾患が、関節リウマチ、全身... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #Summarization #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 日本語特許請求項要約（医薬特定ドメイン限定）\n\n- \"\"\"【請求項１】\n ヒトＣＤ３８（配列番号１）及びカニクイザルＣＤ３８（配列番号２）に特異的に結合する単離された抗体であって、\nａ）以下を含む重鎖可変領域：\n ｉ）配列番号３を... |
text-classification | transformers |
## MarathiSentiment
** An updated and better version of this model covering multiple domains is shared here: <a href="https://huggingface.co/l3cube-pune/marathi-sentiment-md"> marathi-sentiment-md </a> ** <br>
MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi twe... | {"language": "mr", "license": "cc-by-4.0", "tags": ["albert"], "datasets": ["L3CubeMahaSent"], "widget": [{"text": "I like you. </s></s> I love you."}]} | l3cube-pune/MarathiSentiment | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"text-classification",
"mr",
"dataset:L3CubeMahaSent",
"arxiv:2103.11408",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11408"
] | [
"mr"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #text-classification #mr #dataset-L3CubeMahaSent #arxiv-2103.11408 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## MarathiSentiment
An updated and better version of this model covering multiple domains is shared here: <a href="URL marathi-sentiment-md </a> <br>
MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
[dataset link] (URL
... | [
"## MarathiSentiment\n \n An updated and better version of this model covering multiple domains is shared here: <a href=\"URL marathi-sentiment-md </a> <br>\n\nMarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.\n[dataset lin... | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #text-classification #mr #dataset-L3CubeMahaSent #arxiv-2103.11408 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## MarathiSentiment\n \n An updated and better version of this model covering multiple domains is shared here: <... |
text-classification | transformers |
## hate-bert-hasoc-marathi
hate-bert-hasoc-marathi is a binary hate speech model fine-tuned on Marathi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
A new version ... | {"language": "mr", "license": "cc-by-4.0", "tags": ["albert"], "datasets": ["HASOC 2021"], "widget": [{"text": "I like you. </s></s> I love you."}]} | l3cube-pune/hate-bert-hasoc-marathi | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"text-classification",
"mr",
"arxiv:2110.12200",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.12200"
] | [
"mr"
] | TAGS
#transformers #pytorch #tf #safetensors #albert #text-classification #mr #arxiv-2110.12200 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## hate-bert-hasoc-marathi
hate-bert-hasoc-marathi is a binary hate speech model fine-tuned on Marathi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (URL
A new version of Marathi Hate Speech Detecti... | [
"## hate-bert-hasoc-marathi\n\nhate-bert-hasoc-marathi is a binary hate speech model fine-tuned on Marathi Hasoc Hate Speech Dataset 2021.\nThe label mappings are 0 -> None, 1 -> Hate.\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\nA new version of Marathi Hate Spee... | [
"TAGS\n#transformers #pytorch #tf #safetensors #albert #text-classification #mr #arxiv-2110.12200 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## hate-bert-hasoc-marathi\n\nhate-bert-hasoc-marathi is a binary hate speech model fine-tuned on Marathi Hasoc Hate Speech Dataset 2021... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.