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text-classification | transformers | # About this model: Topical Change Detection in Documents
This network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the pap... | {} | dennlinger/roberta-cls-consec | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"text-classification",
"arxiv:2012.03619",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.03619"
] | [] | TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #arxiv-2012.03619 #autotrain_compatible #endpoints_compatible #region-us
| # About this model: Topical Change Detection in Documents
This network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the pap... | [
"# About this model: Topical Change Detection in Documents\nThis network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for t... | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #arxiv-2012.03619 #autotrain_compatible #endpoints_compatible #region-us \n",
"# About this model: Topical Change Detection in Documents\nThis network has been fine-tuned for the task described in the paper *Topical Change Detection in ... |
question-answering | transformers |
# Bilingual English + German SQuAD2.0
We created German Squad 2.0 (**deQuAD 2.0**) and merged with [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) into an English and German training data for question answering. The [**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/mul... | {"language": ["de", "en", "multilingual"], "license": "mit", "tags": ["english", "german"]} | deutsche-telekom/bert-multi-english-german-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"english",
"german",
"de",
"en",
"multilingual",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #english #german #de #en #multilingual #license-mit #endpoints_compatible #has_space #region-us
|
# Bilingual English + German SQuAD2.0
We created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training data for question answering. The bert-base-multilingual-cased is used to fine-tune bilingual QA downstream task.
## Details of deQuAD 2.0
SQuAD2.0 was auto-translated into Germa... | [
"# Bilingual English + German SQuAD2.0\n\nWe created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training data for question answering. The bert-base-multilingual-cased is used to fine-tune bilingual QA downstream task.",
"## Details of deQuAD 2.0\nSQuAD2.0 was auto-translated... | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #english #german #de #en #multilingual #license-mit #endpoints_compatible #has_space #region-us \n",
"# Bilingual English + German SQuAD2.0\n\nWe created German Squad 2.0 (deQuAD 2.0) and merged with SQuAD2.0 into an English and German training ... |
question-answering | transformers |
We released the German Question Answering model fine-tuned with our own German Question Answering dataset (**deQuAD**) containing **130k** training and **11k** test QA pairs.
## Overview
- **Language model:** [electra-base-german-uncased](https://huggingface.co/german-nlp-group/electra-base-german-uncased)
- **Langua... | {"language": "de", "license": "mit", "tags": ["german"]} | deutsche-telekom/electra-base-de-squad2 | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"question-answering",
"german",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #electra #question-answering #german #de #license-mit #endpoints_compatible #region-us
| We released the German Question Answering model fine-tuned with our own German Question Answering dataset (deQuAD) containing 130k training and 11k test QA pairs.
Overview
--------
* Language model: electra-base-german-uncased
* Language: German
* Training data: deQuAD2.0 training set (~42MB)
* Evaluation data: deQ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #question-answering #german #de #license-mit #endpoints_compatible #region-us \n"
] |
summarization | transformers |
# mT5-small-sum-de-en-v1
This is a bilingual summarization model for English and German. It is based on the multilingual T5 model [google/mt5-small](https://huggingface.co/google/mt5-small).
[](https://www.welove.... | {"language": ["de", "en", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum", "wiki_lingua", "mlsum", "swiss_text_2019"]} | deutsche-telekom/mt5-small-sum-de-en-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"de",
"en",
"multilingual",
"dataset:cnn_dailymail",
"dataset:xsum",
"dataset:wiki_lingua",
"dataset:mlsum",
"dataset:swiss_text_2019",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoi... | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #en #multilingual #dataset-cnn_dailymail #dataset-xsum #dataset-wiki_lingua #dataset-mlsum #dataset-swiss_text_2019 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5-small-sum-de-en-v1
======================
This is a bilingual summarization model for English and German. It is based on the multilingual T5 model google/mt5-small.
![One Conversation](URL
This model is provided by the One Conversation
team of Deutsche Telekom AG.
Training
--------
The training was conducte... | [] | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #en #multilingual #dataset-cnn_dailymail #dataset-xsum #dataset-wiki_lingua #dataset-mlsum #dataset-swiss_text_2019 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
... |
summarization | transformers |
# mT5-small-sum-de-mit-v1
This is a German summarization model. It is based on the multilingual T5 model [google/mt5-small](https://huggingface.co/google/mt5-small). The special characteristic of this model is that, unlike many other models, it is licensed under a permissive open source license (MIT). Among other thi... | {"language": ["de"], "license": "mit", "tags": ["summarization"], "datasets": ["swiss_text_2019"]} | deutsche-telekom/mt5-small-sum-de-mit-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"de",
"dataset:swiss_text_2019",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #dataset-swiss_text_2019 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5-small-sum-de-mit-v1
=======================
This is a German summarization model. It is based on the multilingual T5 model google/mt5-small. The special characteristic of this model is that, unlike many other models, it is licensed under a permissive open source license (MIT). Among other things, this license all... | [] | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #de #dataset-swiss_text_2019 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-NER-finetuned-ner
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-N... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["x_glue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-NER-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "x_glue", "type": "x_glu... | deval/bert-base-NER-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:x_glue",
"license:mit",
"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-x_glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-NER-finetuned-ner
===========================
This model is a fine-tuned version of dslim/bert-base-NER on the x\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4380
* Precision: 0.2274
* Recall: 0.1119
* F1: 0.1499
* Accuracy: 0.8485
Model description
-----------------... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["x_glue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "x_glue", "ty... | deval/bert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:x_glue",
"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-x_glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on the x\_glue dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7979
* Precision: 0.0919
* Recall: 0.1249
* F1: 0.1059
* Accuracy: 0.4927
Model description
-----------... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-x_glue #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\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | deval/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0606
* Precision: 0.9277
* Recall: 0.9385
* F1: 0.9330
* Accuracy: 0.9844
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
automatic-speech-recognition | transformers | # Fintuned Wav2Vec of Timit - 4001 checkpoint
| {} | devin132/w2v-timit-ft-4001 | 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
| # Fintuned Wav2Vec of Timit - 4001 checkpoint
| [
"# Fintuned Wav2Vec of Timit - 4001 checkpoint"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# Fintuned Wav2Vec of Timit - 4001 checkpoint"
] |
fill-mask | transformers | # Dummy Model
This be a dummmmmy | {} | devtrent/dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # Dummy Model
This be a dummmmmy | [
"# Dummy Model\n\nThis be a dummmmmy"
] | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Dummy Model\n\nThis be a dummmmmy"
] |
text-classification | transformers | DistilBERT model trained on OSCAR nepali corpus from huggingface datasets.
We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as posti... | {} | dexhrestha/Nepali-DistilBERT | 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
| DistilBERT model trained on OSCAR nepali corpus from huggingface datasets.
We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as posti... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
#Aerith GPT model | {"tags": ["conversational"]} | df4rfrrf/DialoGPT-medium-Aerith | 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
|
#Aerith GPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | This the repo for the final project | {} | dhairya2303/bert-base-uncased-emotion-AD | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This the repo for the final project | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | {'sadness':0,'joy':1,'love':2,'anger':3,'fear':4,'surprise':5} | {} | dhairya2303/bert-base-uncased-emotion_holler | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| {'sadness':0,'joy':1,'love':2,'anger':3,'fear':4,'surprise':5} | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-funsd-test", "results": []}]} | dhanesh123in/layoutlmv2-finetuned-funsd-test | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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",
... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown data... |
text-generation | transformers |
# AMy San | {"tags": ["conversational"]} | dhanushlnaik/amySan | 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
|
# AMy San | [
"# AMy San"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# AMy San"
] |
text-classification | transformers | "hello"
| {} | dhikri/question_answering_glue | 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
| "hello"
| [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # DistilBert Dummy Sentiment Model
## Purpose
This is a dummy model that can be used for testing the transformers `pipeline` with the task `sentiment-analysis`. It should always give random results (i.e. `{"label": "negative", "score": 0.5}`).
## How to use
```python
classifier = pipeline("sentiment-analysis", "dh... | {"language": ["multilingual", "en"], "tags": ["sentiment-analysis", "testing", "unit tests"]} | dhpollack/distilbert-dummy-sentiment | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"sentiment-analysis",
"testing",
"unit tests",
"multilingual",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual",
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #sentiment-analysis #testing #unit tests #multilingual #en #autotrain_compatible #endpoints_compatible #region-us
| # DistilBert Dummy Sentiment Model
## Purpose
This is a dummy model that can be used for testing the transformers 'pipeline' with the task 'sentiment-analysis'. It should always give random results (i.e. '{"label": "negative", "score": 0.5}').
## How to use
## Notes
This was created as follows:
1. Create a URL... | [
"# DistilBert Dummy Sentiment Model",
"## Purpose\n\nThis is a dummy model that can be used for testing the transformers 'pipeline' with the task 'sentiment-analysis'. It should always give random results (i.e. '{\"label\": \"negative\", \"score\": 0.5}').",
"## How to use",
"## Notes\n\nThis was created as ... | [
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"# DistilBert Dummy Sentiment Model",
"## Purpose\n\nThis is a dummy model that can be used for testing the transformers 'pipeline... |
text-classification | transformers | ### TUNiB-Electra Stereotype Detector
Finetuned TUNiB-Electra base with K-StereoSet.
Original Code: https://github.com/newfull5/Stereotype-Detector | {} | dhtocks/tunib-electra-stereotype-classifier | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ### TUNiB-Electra Stereotype Detector
Finetuned TUNiB-Electra base with K-StereoSet.
Original Code: URL | [
"### TUNiB-Electra Stereotype Detector\n\nFinetuned TUNiB-Electra base with K-StereoSet.\n\nOriginal Code: URL"
] | [
"TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### TUNiB-Electra Stereotype Detector\n\nFinetuned TUNiB-Electra base with K-StereoSet.\n\nOriginal Code: URL"
] |
feature-extraction | transformers | Language Model 2
For Language agnostic Dense Passage Retrieval | {} | diarsabri/LaDPR-context-encoder | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us
| Language Model 2
For Language agnostic Dense Passage Retrieval | [] | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | Language Model 1
For Language agnostic Dense Passage Retrieval | {} | diarsabri/LaDPR-query-encoder | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us
| Language Model 1
For Language agnostic Dense Passage Retrieval | [] | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53
---
language: gl
datasets:
- OpenSLR 77
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Galician Wav2Vec2-Large-XLSR-53
results:
- task:
name: Speech Recognition
type: automatic-speech-recogn... | {} | diego-fustes/wav2vec2-large-xlsr-gl | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53
---
language: gl
datasets:
- OpenSLR 77
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Galician Wav2Vec2-Large-XLSR-53
results:
- task:
name: Speech Recognition
type: automatic-speech-recogn... | [
"# Wav2Vec2-Large-XLSR-53\n\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- xlsr-fine-tuning-week\nlicense: apache-2.0\nmodel-index:\n- name: Galician Wav2Vec2-Large-XLSR-53\n results:\n - task: \n name: Speech Recognition\n type:... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53\n\n---\nlanguage: gl\ndatasets:\n- OpenSLR 77\nmetrics:\n- wer\ntags:\n- audio\n- automatic-speech-recognition\n- speech\n- xlsr-fine-tuning-week\nlicense: apache... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned ver... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1", "results": []}]} | diegor2/t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetu-truncated-d22eed | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inf... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned ver... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language ... | diegor2/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetu-truncated-41f800 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-tiny-random-length-96-learning\_rate-2e-05-weight\_decay-0.005-finetuned-en-to-ro-TRAIN\_EPOCHS-1
====================================================================================================
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16\_en\_ro\_pre\_processed dataset.
... | [
"### 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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned vers... | {"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1", "results": []}]} | diegor2/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore info... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1... |
sentence-similarity | transformers |
# Twitter4SSE
This model maps texts to 768 dimensional dense embeddings that encode semantic similarity.
It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset.
It was initialized from [BERTweet](https://huggingface.co/vinai/bertweet-base) and trained with [Sentence-transformers](https://ww... | {"language": ["en"], "license": "apache-2.0", "tags": ["Pytorch", "Sentence Transformers", "Transformers"], "pipeline_tag": "sentence-similarity"} | digio/Twitter4SSE | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"Pytorch",
"Sentence Transformers",
"Transformers",
"sentence-similarity",
"en",
"arxiv:2110.02030",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.02030"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #Pytorch #Sentence Transformers #Transformers #sentence-similarity #en #arxiv-2110.02030 #license-apache-2.0 #endpoints_compatible #region-us
|
# Twitter4SSE
This model maps texts to 768 dimensional dense embeddings that encode semantic similarity.
It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset.
It was initialized from BERTweet and trained with Sentence-transformers.
## Usage
The model is easier to use with sentence-trai... | [
"# Twitter4SSE\n\nThis model maps texts to 768 dimensional dense embeddings that encode semantic similarity. \nIt was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. \nIt was initialized from BERTweet and trained with Sentence-transformers.",
"## Usage\n\nThe model is easier to use with ... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #Pytorch #Sentence Transformers #Transformers #sentence-similarity #en #arxiv-2110.02030 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Twitter4SSE\n\nThis model maps texts to 768 dimensional dense embeddings that encode semantic similarity.... |
zero-shot-classification | transformers |
# COVID-Twitter-BERT v2 MNLI
## Model description
This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.
The technique is based on [Yin et al.](https://arxiv.org/abs/1909.00161).
The article describes a very clever... | {"language": ["en"], "license": "mit", "tags": ["Twitter", "COVID-19", "text-classification", "pytorch", "tensorflow", "bert"], "datasets": ["mnli"], "thumbnail": "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png", "pipeline_tag": "zero-shot-classifi... | digitalepidemiologylab/covid-twitter-bert-v2-mnli | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"Twitter",
"COVID-19",
"tensorflow",
"zero-shot-classification",
"en",
"dataset:mnli",
"arxiv:1909.00161",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.00161"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #Twitter #COVID-19 #tensorflow #zero-shot-classification #en #dataset-mnli #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# COVID-Twitter-BERT v2 MNLI
## Model description
This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.
The technique is based on Yin et al..
The article describes a very clever way of using pre-trained MNLI model... | [
"# COVID-Twitter-BERT v2 MNLI",
"## Model description\nThis model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.\n\nThe technique is based on Yin et al..\nThe article describes a very clever way of using pre-traine... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #Twitter #COVID-19 #tensorflow #zero-shot-classification #en #dataset-mnli #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# COVID-Twitter-BERT v2 MNLI",
"## Model description\nThis model provides a zero-sh... |
null | transformers |
# COVID-Twitter-BERT v2
## Model description
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to [covid-twitter-bert](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert) - but trained on more data, resulting in higher downstream performan... | {"language": "en", "license": "mit", "tags": ["Twitter", "COVID-19"], "thumbnail": "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"} | digitalepidemiologylab/covid-twitter-bert-v2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"Twitter",
"COVID-19",
"en",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #has_space #region-us
|
# COVID-Twitter-BERT v2
## Model description
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter-bert - but trained on more data, resulting in higher downstream performance.
Find more info on our GitHub page.
## Intended uses & limitat... | [
"# COVID-Twitter-BERT v2",
"## Model description\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter-bert - but trained on more data, resulting in higher downstream performance.\n\nFind more info on our GitHub page.",
"## Intended... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #has_space #region-us \n",
"# COVID-Twitter-BERT v2",
"## Model description\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. This model is identical to covid-twitter... |
null | transformers |
# COVID-Twitter-BERT (CT-BERT) v1
:warning: _You may want to use the [v2 model](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) which was trained on more recent data and yields better performance_ :warning:
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-1... | {"language": "en", "license": "mit", "tags": ["Twitter", "COVID-19"], "thumbnail": "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"} | digitalepidemiologylab/covid-twitter-bert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"Twitter",
"COVID-19",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #region-us
|
# COVID-Twitter-BERT (CT-BERT) v1
:warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning:
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our GitHub page.
## Overview
This model was train... | [
"# COVID-Twitter-BERT (CT-BERT) v1\n\n:warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning: \n\n\nBERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our GitHub page.",
"## Overview\nThis m... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #Twitter #COVID-19 #en #license-mit #endpoints_compatible #region-us \n",
"# COVID-Twitter-BERT (CT-BERT) v1\n\n:warning: _You may want to use the v2 model which was trained on more recent data and yields better performance_ :warning: \n\n\nBERT-large-uncased model, pr... |
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. -->
# distilgpt2-finetuned-AdventureTime
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-AT", "results": []}]} | pyordii/distilgpt2-finetuned-AT | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-AdventureTime
==================================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2450
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: 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: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
fill-mask | transformers | fBERT: A Neural Transformer for Identifying Offensive Content [Accepted at EMNLP 2021]
Authors: Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe and Alexander Ororbia
About:
Transformer-based models such as BERT, ELMO, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the i... | {} | diptanu/fBERT | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| fBERT: A Neural Transformer for Identifying Offensive Content [Accepted at EMNLP 2021]
Authors: Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe and Alexander Ororbia
About:
Transformer-based models such as BERT, ELMO, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the i... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Moe DialoGPT Model | {"tags": ["conversational"]} | disdamoe/DialoGPT-small-moe | 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
|
# Moe DialoGPT Model | [
"# Moe DialoGPT Model"
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"# Moe DialoGPT Model"
] |
text-generation | transformers |
# Moe DialoGPT Model | {"tags": ["conversational"]} | disdamoe/TheGreatManipulator | 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
|
# Moe DialoGPT Model | [
"# Moe DialoGPT Model"
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"# Moe DialoGPT Model"
] |
text-generation | transformers |
# The Manipulator | {"tags": ["conversational"]} | disdamoe/TheManipulator | 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
|
# The Manipulator | [
"# The Manipulator"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# The Manipulator"
] |
null | null | <a href="https://www.geogebra.org/m/w8uzjttg">.</a>
<a href="https://www.geogebra.org/m/gvn7m78g">.</a>
<a href="https://www.geogebra.org/m/arxecanq">.</a>
<a href="https://www.geogebra.org/m/xb69bvww">.</a>
<a href="https://www.geogebra.org/m/apvepfnd">.</a>
<a href="https://www.geogebra.org/m/evmj8ckk">.</a>
<a href=... | {} | dispenst/hgfytgfg | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| <a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href="URL
<a href=... | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers | We took `facebook/wav2vec2-large-960h` and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transc... | {"language": "en", "license": "mit", "tags": ["audio", "automatic-speech-recognition"], "metrics": ["wer"]} | distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #license-mit #endpoints_compatible #region-us
| We took 'facebook/wav2vec2-large-960h' and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transc... | [
"## Usage"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #license-mit #endpoints_compatible #region-us \n",
"## Usage"
] |
text-generation | null |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | divi/Peterbot | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Peter from Your Boyfriend Game.
| [
"# Peter from Your Boyfriend Game."
] | [
"TAGS\n#conversational #region-us \n",
"# Peter from Your Boyfriend Game."
] |
text-classification | transformers |
# diwank/dyda-deberta-pair
Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exa... | {"license": "mit"} | diwank/dyda-deberta-pair | null | [
"transformers",
"pytorch",
"tf",
"deberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# diwank/dyda-deberta-pair
Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exa... | [
"# diwank/dyda-deberta-pair\r\n\r\nDeberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labe... | [
"TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# diwank/dyda-deberta-pair\r\n\r\nDeberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current o... |
text-classification | transformers |
# maptask-deberta-pair
Deberta-based Daily MapTask style dialog-act annotations classification model
## Example
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/maptask-deberta-pair")
predictions, raw... | {"license": "mit"} | diwank/maptask-deberta-pair | null | [
"transformers",
"pytorch",
"tf",
"deberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# maptask-deberta-pair
Deberta-based Daily MapTask style dialog-act annotations classification model
## Example
| [
"# maptask-deberta-pair\r\nDeberta-based Daily MapTask style dialog-act annotations classification model",
"## Example"
] | [
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"# maptask-deberta-pair\r\nDeberta-based Daily MapTask style dialog-act annotations classification model",
"## Example"
] |
text-classification | transformers |
# diwank/silicone-deberta-pair
`deberta-base`-based dialog acts classifier. Trained on the `balanced` variant of the [silicone-merged](https://huggingface.co/datasets/diwank/silicone-merged) dataset: a simplified merged dialog act data from datasets in the [silicone](https://huggingface.co/datasets/silicone) colle... | {"license": "mit"} | diwank/silicone-deberta-pair | null | [
"transformers",
"pytorch",
"tf",
"deberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# diwank/silicone-deberta-pair
'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection.
Takes two sentences as inputs (one previous and one current utterance of a dialog). The pre... | [
"# diwank/silicone-deberta-pair\r\n\r\n'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog act data from datasets in the silicone collection. \r\n\r\nTakes two sentences as inputs (one previous and one current utterance of a dialo... | [
"TAGS\n#transformers #pytorch #tf #deberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# diwank/silicone-deberta-pair\r\n\r\n'deberta-base'-based dialog acts classifier. Trained on the 'balanced' variant of the silicone-merged dataset: a simplified merged dialog ... |
null | transformers | Slavic BERT from https://github.com/deepmipt/Slavic-BERT-NER http://files.deeppavlov.ai/deeppavlov_data/bg_cs_pl_ru_cased_L-12_H-768_A-12.tar.gz
| {} | djstrong/bg_cs_pl_ru_cased_L-12_H-768_A-12 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| Slavic BERT from URL URL
| [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | dk16gaming/DialoGPT-small-HarryPotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
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"# Harry Potter DialoGPT Model"
] |
text-classification | transformers | ### Bert-News | {} | dkhara/bert-news | 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
| ### Bert-News | [
"### Bert-News"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Bert-News"
] |
null | transformers |
# Polbert - Polish BERT
Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below.
![PolBERT image](https://ra... | {"language": "pl", "thumbnail": "https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png"} | dkleczek/bert-base-polish-cased-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"pl",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #bert #pretraining #pl #endpoints_compatible #has_space #region-us
| Polbert - Polish BERT
=====================
Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below.
!PolBE... | [
"### Uncased",
"### Cased\n\n\n\nPre-training details\n--------------------",
"### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training ... | [
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"### Cased\n\n\n\nPre-training details\n--------------------",
"### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released mode... |
fill-mask | transformers |
# Polbert - Polish BERT
Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below.
![PolBERT image](https://ra... | {"language": "pl", "thumbnail": "https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png"} | dkleczek/bert-base-polish-uncased-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"pl",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #pl #autotrain_compatible #endpoints_compatible #has_space #region-us
| Polbert - Polish BERT
=====================
Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below.
!PolBE... | [
"### Uncased",
"### Cased\n\n\n\nPre-training details\n--------------------",
"### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)\n* Training ... | [
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"### Uncased\n\n\n* Polbert was trained with code provided in Google BERT's github repository (URL\n* Cur... |
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. -->
# papuGaPT2-finetuned-wierszyki
This model is a fine-tuned version of [flax-community/papuGaPT2](https://huggingface.co/flax-commu... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "papuGaPT2-finetuned-wierszyki", "results": []}]} | dkleczek/papuGaPT2-finetuned-wierszyki | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| papuGaPT2-finetuned-wierszyki
=============================
This model is a fine-tuned version of flax-community/papuGaPT2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8122
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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 #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batc... |
text-generation | transformers |
# papuGaPT2 - Polish GPT2 language model
[GPT2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation mode... | {"language": "pl", "tags": ["text-generation"], "widget": [{"text": "Najsmaczniejszy polski owoc to"}]} | dkleczek/papuGaPT2 | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"pl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #pl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# papuGaPT2 - Polish GPT2 language model
GPT2 was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this ... | [
"# papuGaPT2 - Polish GPT2 language model\nGPT2 was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of ... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #pl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# papuGaPT2 - Polish GPT2 language model\nGPT2 was released in 2019 and surprised many with its text generation capability. However, up until very rece... |
text-generation | transformers |
# A certain person's AI | {"tags": ["conversational"]} | dkminer81/Tromm | 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
|
# A certain person's AI | [
"# A certain person's AI"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# A certain person's AI"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-demo-colab", "results": []}]} | dkssud/wav2vec2-base-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-demo-colab
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4171
* Wer: 0.3452
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
question-answering | transformers | # OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001
This is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question ... | {} | dkurt/bert-large-uncased-whole-word-masking-squad-int8-0001 | null | [
"transformers",
"bert",
"question-answering",
"arxiv:1810.04805",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us
| # OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001
This is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question ... | [
"# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001\n\nThis is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and qu... | [
"TAGS\n#transformers #bert #question-answering #arxiv-1810.04805 #endpoints_compatible #region-us \n",
"# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001\n\nThis is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training se... |
audio-classification | transformers | [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) model quantized with [Optimum OpenVINO](https://github.com/dkurt/optimum-openvino/).
| Accuracy on eval (baseline) | Accuracy on eval (quantized) |
|-----------------------------|---------------------------... | {} | dkurt/wav2vec2-base-ft-keyword-spotting-int8 | null | [
"transformers",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #wav2vec2 #audio-classification #endpoints_compatible #region-us
| anton-l/wav2vec2-base-ft-keyword-spotting model quantized with Optimum OpenVINO.
| [] | [
"TAGS\n#transformers #wav2vec2 #audio-classification #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | dmiller1/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2161
* Accuracy: 0.926
* F1: 0.9261
Model description
-----------------
Mor... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #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\\_rate: 2... |
null | transformers | NER Model of BERN2 system
| {} | dmis-lab/bern2-ner | null | [
"transformers",
"pytorch",
"roberta",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #endpoints_compatible #region-us
| NER Model of BERN2 system
| [] | [
"TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Model Card for biobert-large-cased-v1.1-squad
# Model Details
## Model Description
More information needed
- **Developed by:** DMIS-lab (Data Mining and Information Systems Lab, Korea University)
- **Shared by [Optional]:** DMIS-lab (Data Mining and Information Systems Lab, Korea University)
- **Model type... | {"tags": ["question-answering", "bert"]} | dmis-lab/biobert-large-cased-v1.1-squad | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"arxiv:1901.08746",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.08746",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
|
# Model Card for biobert-large-cased-v1.1-squad
# Model Details
## Model Description
More information needed
- Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University)
- Shared by [Optional]: DMIS-lab (Data Mining and Information Systems Lab, Korea University)
- Model type: Question... | [
"# Model Card for biobert-large-cased-v1.1-squad",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: DMIS-lab (Data Mining and Information Systems Lab, Korea University)\n\n- Mode... | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n",
"# Model Card for biobert-large-cased-v1.1-squad",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: DMIS-lab (Data Mining an... |
feature-extraction | transformers | hello
| {} | dmis-lab/biosyn-biobert-bc2gn | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | hello
| {} | dmis-lab/biosyn-sapbert-bc2gn | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# Model Card for biosyn-sapbert-ncbi-disease
# Model Details
## Model Description
More information needed
- **Developed by:** Dmis-lab (Data Mining and Information Systems Lab, Korea University)
- **Shared by [Optional]:** Hugging Face
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More info... | {"tags": ["bert"]} | dmis-lab/biosyn-sapbert-ncbi-disease | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:1901.08746",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1901.08746",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for biosyn-sapbert-ncbi-disease
# Model Details
## Model Description
More information needed
- Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University)
- Shared by [Optional]: Hugging Face
- Model type: Feature Extraction
- Language(s) (NLP): More information needed
-... | [
"# Model Card for biosyn-sapbert-ncbi-disease",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University)\n- Shared by [Optional]: Hugging Face\n- Model type: Feature Extraction\n- Language(s) (NLP): More informa... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for biosyn-sapbert-ncbi-disease",
"# Model Details",
"## Model Description\n \nMore information needed\n \n- Developed by: Dmis-lab (Data Mining and Information Syste... |
summarization | transformers |
# rubert_ria_headlines
## Description
*bert2bert* model, initialized with the `DeepPavlov/rubert-base-cased` pretrained weights and
fine-tuned on the first 99% of ["Rossiya Segodnya" news dataset](https://github.com/RossiyaSegodnya/ria_news_dataset) for 2 epochs.
## Usage example
```python
from transformers imp... | {"language": ["ru"], "license": "mit", "tags": ["summarization", "bert", "rubert"]} | dmitry-vorobiev/rubert_ria_headlines | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"bert",
"rubert",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #bert #rubert #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# rubert_ria_headlines
## Description
*bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained weights and
fine-tuned on the first 99% of "Rossiya Segodnya" news dataset for 2 epochs.
## Usage example
## Datasets
- ria_news
## How it was trained?
I used free TPUv3 on kaggle. The ... | [
"# rubert_ria_headlines",
"## Description\n*bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained weights and \n fine-tuned on the first 99% of \"Rossiya Segodnya\" news dataset for 2 epochs.",
"## Usage example",
"## Datasets\n- ria_news",
"## How it was trained?\n\nI used free... | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #summarization #bert #rubert #ru #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# rubert_ria_headlines",
"## Description\n*bert2bert* model, initialized with the 'DeepPavlov/rubert-base-cased' pretrained... |
text2text-generation | transformers |
# doc2query/S2ORC-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 querie... | {"language": "en", "license": "apache-2.0", "datasets": ["S2ORC"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approa... | doc2query/S2ORC-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:S2ORC",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-S2ORC #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/S2ORC-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The gene... | [
"# doc2query/S2ORC-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Luc... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-S2ORC #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/S2ORC-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5q... |
text2text-generation | transformers |
# doc2query/all-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries ... | {"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/reddit-title-body", "sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notabl... | doc2query/all-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/reddit-title-body",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-gener... | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/all-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The genera... | [
"# doc2query/all-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucen... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2... |
text2text-generation | transformers |
# doc2query/all-with_prefix-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20... | {"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/reddit-title-body", "sentence-transformers/embedding-training-data"], "widget": [{"text": "text2reddit: Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability wi... | doc2query/all-with_prefix-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/reddit-title-body",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space"... | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| doc2query/all-with\_prefix-t5-base-v1
=====================================
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
* Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like El... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/reddit-title-body #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"... |
text2text-generation | transformers |
# doc2query/msmarco-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 quer... | {"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its langua... | doc2query/msmarco-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The ge... | [
"# doc2query/msmarco-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or L... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-t5-base-v1\r\n\r\nThis is a doc2que... |
text2text-generation | transformers |
# doc2query/msmarco-t5-small-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 que... | {"language": "en", "license": "apache-2.0", "datasets": ["sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its langua... | doc2query/msmarco-t5-small-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/msmarco-t5-small-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The g... | [
"# doc2query/msmarco-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-sentence-transformers/embedding-training-data #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/msmarco-t5-small-v1\r\n\r\nThis is a doc2qu... |
text2text-generation | transformers |
# doc2query/reddit-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queri... | {"language": "en", "license": "apache-2.0", "datasets": ["datasets/sentence-transformers/reddit-title-body"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its lan... | doc2query/reddit-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/reddit-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The gen... | [
"# doc2query/reddit-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lu... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/reddit-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\n... |
text2text-generation | transformers |
# doc2query/reddit-t5-small-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 quer... | {"language": "en", "license": "apache-2.0", "datasets": ["datasets/sentence-transformers/reddit-title-body"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its lan... | doc2query/reddit-t5-small-v1 | null | [
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"t5",
"text2text-generation",
"en",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/reddit-t5-small-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The ge... | [
"# doc2query/reddit-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or L... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/reddit-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\... |
text2text-generation | transformers |
# doc2query/stackexchange-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-4... | {"language": "en", "license": "apache-2.0", "datasets": ["flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant... | doc2query/stackexchange-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/stackexchange-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. ... | [
"# doc2query/stackexchange-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/stackexchange-t5-bas... |
text2text-generation | transformers |
# doc2query/stackexchange-title-body-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your para... | {"language": "en", "license": "apache-2.0", "datasets": ["flax-sentence-embeddings/stackexchange_title_body_jsonl"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. ... | doc2query/stackexchange-title-body-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:flax-sentence-embeddings/stackexchange_title_body_jsonl",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/stackexchange-title-body-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, ... | [
"# doc2query/stackexchange-title-body-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch,... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/stackexchange-title-body-t5-base-... |
text2text-generation | transformers |
# doc2query/stackexchange-title-body-t5-small-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your par... | {"language": "en", "license": "apache-2.0", "datasets": ["flax-sentence-embeddings/stackexchange_title_body_jsonl"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. ... | doc2query/stackexchange-title-body-t5-small-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:flax-sentence-embeddings/stackexchange_title_body_jsonl",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# doc2query/stackexchange-title-body-t5-small-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch,... | [
"# doc2query/stackexchange-title-body-t5-small-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-flax-sentence-embeddings/stackexchange_title_body_jsonl #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# doc2query/stackexchange-title-body-t5-small... |
text2text-generation | transformers |
# doc2query/yahoo_answers-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-4... | {"language": "en", "license": "apache-2.0", "datasets": ["datasets/sentence-transformers/embedding-training-data"], "widget": [{"text": "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. I... | doc2query/yahoo_answers-t5-base-v1 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.08375",
"2104.08663"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# doc2query/yahoo_answers-t5-base-v1
This is a doc2query model based on T5 (also known as docT5query).
It can be used for:
- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. ... | [
"# doc2query/yahoo_answers-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as docT5query).\r\n\r\nIt can be used for:\r\n- Document expansion: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-1904.08375 #arxiv-2104.08663 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# doc2query/yahoo_answers-t5-base-v1\r\n\r\nThis is a doc2query model based on T5 (also known as do... |
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | domdomreloaded/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-swag
================================
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6045
* Accuracy: 0.7960
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 5e-05\n* train\\_batch\\_size: 16\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conl... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "... | dominiqueblok/roberta-base-finetuned-ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0492
* Precision: 0.9530
* Recall: 0.9604
* F1: 0.9567
* Accuracy: 0.9889
Model description
-----------------
More... | [
"### 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 #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #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*... |
null | null | # this is a shit model | {} | douglas0204/shitmodel | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # this is a shit model | [
"# this is a shit model"
] | [
"TAGS\n#region-us \n",
"# this is a shit model"
] |
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. -->
# finetune-clm-employment
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetune-clm-employment", "results": []}]} | dpasch01/finetune-clm-employment | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetune-clm-employment
=======================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8445
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: 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 #roberta #fill-mask #generated_from_trainer #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\\_batch\\_size: ... |
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. -->
# finetune-data-skills
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetune-data-skills", "results": []}]} | dpasch01/finetune-data-skills | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetune-data-skills
====================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1058
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
M... | [
"### 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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\\_batch\\_size: 8\n... |
image-classification | transformers |
# Infrastructures
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugg... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | drab/Infrastructures | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Infrastructures
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Cooling tower
!Cooling tower
#### Transmission grid
!Transmission grid
#### Wind turbines
!Wind turb... | [
"# Infrastructures\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Cooling tower\n\n!Cooling tower",
"#### Transmission grid\n\n!Transmission grid",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Infrastructures\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issu... |
null | transformers | 这是一个git lfs项目。
没有改造数据前的模型性能：
knowledge points - max length is 1566, min length is 3, ave length is 87.96, 95% quantile is 490.
question and answer - max length is 303, min length is 8, ave length is 47.09, 95% quantile is 119.
303精度为：2562/5232=48.97%
| {} | dragonStyle/bert-303-step35000 | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| 这是一个git lfs项目。
没有改造数据前的模型性能：
knowledge points - max length is 1566, min length is 3, ave length is 87.96, 95% quantile is 490.
question and answer - max length is 303, min length is 8, ave length is 47.09, 95% quantile is 119.
303精度为：2562/5232=48.97%
| [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Base-Pretrain-Vietnamese
The base model is pre-trained on 16kHz sampled speech audio from 100h Vietnamese unlabelled data in [VLSP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?usp=sharing). When using the model make sure that your speech input is also sampled at 16Khz. Note... | {"language": "vi", "license": "apache-2.0", "tags": ["speech", "automatic-speech-recognition"], "datasets": ["vlsp"]} | dragonSwing/viwav2vec2-base-100h | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"automatic-speech-recognition",
"vi",
"dataset:vlsp",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"vi"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #automatic-speech-recognition #vi #dataset-vlsp #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| # Wav2Vec2-Base-Pretrain-Vietnamese
The base model is pre-trained on 16kHz sampled speech audio from 100h Vietnamese unlabelled data in VLSP dataset. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Vietnamese Automatic ... | [
"# Wav2Vec2-Base-Pretrain-Vietnamese\nThe base model is pre-trained on 16kHz sampled speech audio from 100h Vietnamese unlabelled data in VLSP dataset. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Vietnamese Auto... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #automatic-speech-recognition #vi #dataset-vlsp #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-Pretrain-Vietnamese\nThe base model is pre-trained on 16kHz sampled speech audio from 100h Vietnamese unlabelled... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [dragonSwing/wav2vec2-base-pretrain-vietnamese](https://huggingface.co/dragonSwing/wav2vec2-base-pretrain-vietnamese) on Vietnamese Speech Recognition task using 100h labelled data from [VSLP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?u... | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["vlsp", "common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2vec2 Base Vietnamese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": ... | dragonSwing/wav2vec2-base-vietnamese | null | [
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"automatic-speech-recognition",
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"speech",
"vi",
"dataset:vlsp",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #vi #dataset-vlsp #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned dragonSwing/wav2vec2-base-pretrain-vietnamese on Vietnamese Speech Recognition task using 100h labelled data from VSLP dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) a... | [
"# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned dragonSwing/wav2vec2-base-pretrain-vietnamese on Vietnamese Speech Recognition task using 100h labelled data from VSLP dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a lang... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #vi #dataset-vlsp #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned dragonSwing/wav2vec2-base-pretrain-vietnamese on Vietnam... |
automatic-speech-recognition | speechbrain | # Wav2Vec2-Base-Vietnamese-270h
Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including [Common Voice](https://huggingface.co/datasets/common_voice), [VIVOS](https://huggingface.co/datasets/vivos), [VLSP2020](https://vlsp.org.vn/vlsp2020/e... | {"language": "vi", "license": "cc-by-nc-4.0", "tags": ["audio", "speech", "speechbrain", "Transformer"], "datasets": ["vivos", "common_voice"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Example 1", "src": "https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/r... | dragonSwing/wav2vec2-base-vn-270h | null | [
"speechbrain",
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"audio",
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"Transformer",
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"vi",
"dataset:vivos",
"dataset:common_voice",
"license:cc-by-nc-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#speechbrain #wav2vec2 #audio #speech #Transformer #automatic-speech-recognition #vi #dataset-vivos #dataset-common_voice #license-cc-by-nc-4.0 #model-index #has_space #region-us
| Wav2Vec2-Base-Vietnamese-270h
=============================
Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including Common Voice, VIVOS, VLSP2020. The model was fine-tuned using SpeechBrain toolkit with a custom tokenizer. For a better e... | [
"### Benchmark WER result:\n\n\n\nThe language model was trained using OSCAR dataset on about 32GB of crawled text.",
"### Install SpeechBrain\n\n\nTo use this model, you should install speechbrain > 0.5.10",
"### Usage\n\n\nThe model can be used directly (without a language model) as follows:",
"### Inferenc... | [
"TAGS\n#speechbrain #wav2vec2 #audio #speech #Transformer #automatic-speech-recognition #vi #dataset-vivos #dataset-common_voice #license-cc-by-nc-4.0 #model-index #has_space #region-us \n",
"### Benchmark WER result:\n\n\n\nThe language model was trained using OSCAR dataset on about 32GB of crawled text.",
"##... |
fill-mask | transformers |
# ALBert
The ALR-Bert , **cased** model for Romanian, trained on a 15GB corpus!
ALR-BERT is a multi-layer bidirectional Transformer encoder that shares ALBERT's factorized embedding parameterization and cross-layer sharing. ALR-BERT-base inherits ALBERT-base and features 12 parameter-sharing layers, a 128-dimension ... | {"language": "ro"} | dragosnicolae555/ALR_BERT | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"ro",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #albert #fill-mask #ro #autotrain_compatible #endpoints_compatible #region-us
| ALBert
======
The ALR-Bert , cased model for Romanian, trained on a 15GB corpus!
ALR-BERT is a multi-layer bidirectional Transformer encoder that shares ALBERT's factorized embedding parameterization and cross-layer sharing. ALR-BERT-base inherits ALBERT-base and features 12 parameter-sharing layers, a 128-dimension ... | [
"### How to use\n\n\nRemember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :\n\n\nbecause the model was NOT trained on cedilla ''s'' and ''t''s. If you don't, you will have decreased performance due to s and increased number of tokens per word.",
"### Evaluation\n\n\... | [
"TAGS\n#transformers #pytorch #albert #fill-mask #ro #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nRemember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :\n\n\nbecause the model was NOT trained on cedilla ''s'' and ''t''s. If you do... |
null | null |
Pretrained model on Dagaare language using a masked language modeling (MLM) objective first 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)\
| {"datasets": ["Bible"]} | drcod/DagaareBERTa | null | [
"pytorch",
"tf",
"dataset:Bible",
"arxiv:1907.11692",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [] | TAGS
#pytorch #tf #dataset-Bible #arxiv-1907.11692 #region-us
|
Pretrained model on Dagaare language using a masked language modeling (MLM) objective first introduced in
this paper and first released in
this repository\
| [] | [
"TAGS\n#pytorch #tf #dataset-Bible #arxiv-1907.11692 #region-us \n"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | dreamline2/DialoGPT-small-joshua-demo | null | [
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"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
|
# My Awesome Model | [
"# My Awesome Model"
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"# My Awesome Model"
] |
text-classification | transformers | This is just a test | {} | dreji18/mymodel | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| This is just a test | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29797722
- CO2 Emissions (in grams): 2.7516207978192737
## Validation Metrics
- Loss: 0.6113826036453247
- Accuracy: 0.7559139784946236
- Macro F1: 0.4594734612976928
- Micro F1: 0.7559139784946236
- Weighted F1: 0.7195080232106192... | {"language": "en", "tags": "autonlp", "datasets": ["ds198799/autonlp-data-predict_ROI_1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.7516207978192737} | ds198799/autonlp-predict_ROI_1-29797722 | null | [
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"dataset:ds198799/autonlp-data-predict_ROI_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-ds198799/autonlp-data-predict_ROI_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29797722
- CO2 Emissions (in grams): 2.7516207978192737
## Validation Metrics
- Loss: 0.6113826036453247
- Accuracy: 0.7559139784946236
- Macro F1: 0.4594734612976928
- Micro F1: 0.7559139784946236
- Weighted F1: 0.7195080232106192... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29797722\n- CO2 Emissions (in grams): 2.7516207978192737",
"## Validation Metrics\n\n- Loss: 0.6113826036453247\n- Accuracy: 0.7559139784946236\n- Macro F1: 0.4594734612976928\n- Micro F1: 0.7559139784946236\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-ds198799/autonlp-data-predict_ROI_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29797722\n- CO2 Emissions (in g... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29797730
- CO2 Emissions (in grams): 2.2439127664461718
## Validation Metrics
- Loss: 0.6314184069633484
- Accuracy: 0.7596774193548387
- Macro F1: 0.4740565300039588
- Micro F1: 0.7596774193548386
- Weighted F1: 0.7371623804622154... | {"language": "en", "tags": "autonlp", "datasets": ["ds198799/autonlp-data-predict_ROI_1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.2439127664461718} | ds198799/autonlp-predict_ROI_1-29797730 | null | [
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"roberta",
"text-classification",
"autonlp",
"en",
"dataset:ds198799/autonlp-data-predict_ROI_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-ds198799/autonlp-data-predict_ROI_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29797730
- CO2 Emissions (in grams): 2.2439127664461718
## Validation Metrics
- Loss: 0.6314184069633484
- Accuracy: 0.7596774193548387
- Macro F1: 0.4740565300039588
- Micro F1: 0.7596774193548386
- Weighted F1: 0.7371623804622154... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29797730\n- CO2 Emissions (in grams): 2.2439127664461718",
"## Validation Metrics\n\n- Loss: 0.6314184069633484\n- Accuracy: 0.7596774193548387\n- Macro F1: 0.4740565300039588\n- Micro F1: 0.7596774193548386\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-ds198799/autonlp-data-predict_ROI_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29797730\n- CO2 Emissions (i... |
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": ["conll2002"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2002", "type": "c... | dshvadskiy/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2002",
"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-conll2002 #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 conll2002 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1458
* Precision: 0.7394
* Recall: 0.7884
* F1: 0.7631
* Accuracy: 0.9656
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-conll2002 #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... |
token-classification | transformers |
This model can be used to more accurately detokenize the moses tokenizer (it does a better job with certain lossy quotes and things)
batched usage:
```python
sentences = [
"They 're a young team . they have great players and amazing freshmen coming in , so think they 'll grow into themselves next year ,",
... | {"language": "en", "widget": [{"text": "They 're a young team . they have great players and amazing freshmen coming in , so think they 'll grow into themselves next year ,"}, {"text": "\" We 'll talk go by now ; \" says Shucksmith ;"}, {"text": "\" Warren Gatland is a professional person and it wasn 't a case of 's I '... | dsilin/detok-deberta-xl | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
This model can be used to more accurately detokenize the moses tokenizer (it does a better job with certain lossy quotes and things)
batched usage:
| [] | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | # bert-base-NER
## Model description
**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscell... | {"language": "en", "license": "mit", "datasets": ["conll2003"], "model-index": [{"name": "dslim/bert-base-NER", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "test"}, "metrics": [{"type": "accu... | dslim/bert-base-NER | null | [
"transformers",
"pytorch",
"tf",
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"onnx",
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"arxiv:1810.04805",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #onnx #safetensors #bert #token-classification #en #dataset-conll2003 #arxiv-1810.04805 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-NER
=============
Model description
-----------------
bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person ... | [
"### Available NER models\n\n\nModel Name: distilbert-NER (NEW!), Description: Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT, Parameters: 66M\nModel Name: bert-large-NER, Description: Fine-tuned bert-large-cased - larger model with slightly better performance, Parameters: 340M\nModel Name: bert... | [
"TAGS\n#transformers #pytorch #tf #jax #onnx #safetensors #bert #token-classification #en #dataset-conll2003 #arxiv-1810.04805 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Available NER models\n\n\nModel Name: distilbert-NER (NEW!), Description: Fine-tuned ... |
token-classification | transformers | # bert-large-NER
## Model description
**bert-large-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Misce... | {"language": "en", "license": "mit", "datasets": ["conll2003"], "model-index": [{"name": "dslim/bert-large-NER", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "test"}, "metrics": [{"type": "acc... | dslim/bert-large-NER | null | [
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"license:mit",
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #onnx #safetensors #bert #token-classification #en #dataset-conll2003 #arxiv-1810.04805 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-large-NER
==============
Model description
-----------------
bert-large-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), pers... | [
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the m... | [
"TAGS\n#transformers #pytorch #tf #jax #onnx #safetensors #bert #token-classification #en #dataset-conll2003 #arxiv-1810.04805 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.",
"##... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36839110
- CO2 Emissions (in grams): 123.79523392848652
## Validation Metrics
- Loss: 0.17188367247581482
- Accuracy: 0.9714953271028037
- Precision: 0.9917948717948718
- Recall: 0.9480392156862745
- AUC: 0.9947452731092438
- F1: 0.9694... | {"language": "unk", "tags": "autonlp", "datasets": ["dtam/autonlp-data-covid-fake-news"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 123.79523392848652} | dtam/autonlp-covid-fake-news-36839110 | null | [
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"text-classification",
"autonlp",
"unk",
"dataset:dtam/autonlp-data-covid-fake-news",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #albert #text-classification #autonlp #unk #dataset-dtam/autonlp-data-covid-fake-news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36839110
- CO2 Emissions (in grams): 123.79523392848652
## Validation Metrics
- Loss: 0.17188367247581482
- Accuracy: 0.9714953271028037
- Precision: 0.9917948717948718
- Recall: 0.9480392156862745
- AUC: 0.9947452731092438
- F1: 0.9694... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 36839110\n- CO2 Emissions (in grams): 123.79523392848652",
"## Validation Metrics\n\n- Loss: 0.17188367247581482\n- Accuracy: 0.9714953271028037\n- Precision: 0.9917948717948718\n- Recall: 0.9480392156862745\n- AUC: 0.99474527310... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autonlp #unk #dataset-dtam/autonlp-data-covid-fake-news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 36839110\n- CO2 Emissions (in grams... |
text-classification | transformers |
# RoBERTa base finetuned for Spanish irony detection
## Model description
Model to perform irony detection in Spanish. This is a finetuned version of the [RoBERTa-base-bne model](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the [IroSvA](https://www.autoritas.net/IroSvA2019/) corpus. Only the Spanish fro... | {"language": ["es"], "tags": ["irony", "sarcasm", "spanish"], "widget": [{"text": "\u00a1C\u00f3mo disfruto pele\u00e1ndome con los Transformers!", "example_title": "Ironic"}, {"text": "Madrid es la capital de Espa\u00f1a", "example_title": "Non ironic"}]} | dtomas/roberta-base-bne-irony | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"irony",
"sarcasm",
"spanish",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #roberta #text-classification #irony #sarcasm #spanish #es #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa base finetuned for Spanish irony detection
## Model description
Model to perform irony detection in Spanish. This is a finetuned version of the RoBERTa-base-bne model on the IroSvA corpus. Only the Spanish from Spain variant was used in the training process. It comprises 2,400 tweets labeled as ironic/non-... | [
"# RoBERTa base finetuned for Spanish irony detection",
"## Model description\n\nModel to perform irony detection in Spanish. This is a finetuned version of the RoBERTa-base-bne model on the IroSvA corpus. Only the Spanish from Spain variant was used in the training process. It comprises 2,400 tweets labeled as i... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #irony #sarcasm #spanish #es #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa base finetuned for Spanish irony detection",
"## Model description\n\nModel to perform irony detection in Spanish. This is a finetuned version of the Ro... |
fill-mask | transformers | <h1>BERT for Vietnamese Law</h1>
Apply for Task 1: Legal Document Retrieval on <a href="https://www.jaist.ac.jp/is/labs/nguyen-lab/home/alqac-2021/">ALQAC 2021</a> dataset
The model achieved 0.80 on the leaderboard(1st place score is 0.88).
We use <a href="https://huggingface.co/NlpHUST/vibert4news-base-cased">viber... | {} | ductuan024/AimeLaw | null | [
"transformers",
"pytorch",
"ibert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #ibert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| <h1>BERT for Vietnamese Law</h1>
Apply for Task 1: Legal Document Retrieval on <a href="URL 2021</a> dataset
The model achieved 0.80 on the leaderboard(1st place score is 0.88).
We use <a href="URL as based model and fine-tune on our own Vietnamese law dataset.
We use word sentencepiece, use basic bert tokenization... | [] | [
"TAGS\n#transformers #pytorch #ibert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# RDBotv1 DialoGPT Model | {"tags": ["conversational"]} | dukeme/DialoGPT-small-RDBotv1 | 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
|
# RDBotv1 DialoGPT Model | [
"# RDBotv1 DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RDBotv1 DialoGPT Model"
] |
fill-mask | transformers |
# bert-base-romanian-cased-v1
The BERT **base**, **cased** model for Romanian, trained on a 15GB corpus, version 
### How to use
```python
from transformers import AutoTokenizer, AutoModel
import torch
# load tokenizer and model
tokenizer = AutoTokeni... | {"language": "ro", "license": "mit", "tags": ["bert", "fill-mask"]} | dumitrescustefan/bert-base-romanian-cased-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ro",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ro #license-mit #endpoints_compatible #has_space #region-us
| bert-base-romanian-cased-v1
===========================
The BERT base, cased model for Romanian, trained on a 15GB corpus, version !v1.0
### How to use
Remember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :
because the model was NOT trained on cedilla ''s'' and ''... | [
"### How to use\n\n\nRemember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :\n\n\nbecause the model was NOT trained on cedilla ''s'' and ''t''s. If you don't, you will have decreased performance due to ''''s and increased number of tokens per word.",
"### Evaluation\... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ro #license-mit #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nRemember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :\n\n\nbecause the model was NOT trained on cedilla ''s'' and ''t''s. If y... |
token-classification | transformers | # bert-base-romanian-ner
Updated: 21.01.2022
## Model description
**bert-base-romanian-ner** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize **15** types of entities: persons, geo-politic... | {"language": "ro", "license": "mit", "datasets": ["ronec"]} | dumitrescustefan/bert-base-romanian-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"ro",
"dataset:ronec",
"arxiv:1909.01247",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.01247"
] | [
"ro"
] | TAGS
#transformers #pytorch #bert #token-classification #ro #dataset-ronec #arxiv-1909.01247 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-romanian-ner
======================
Updated: 21.01.2022
Model description
-----------------
bert-base-romanian-ner is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize 15 types of entitie... | [
"### How to use\n\n\nThere are 2 ways to use this model:",
"#### Directly in Transformers:\n\n\nYou can use this model with Transformers *pipeline* for NER; you will have to handle word tokenization in multiple subtokens cases with different labels.",
"#### Use in a Python package\n\n\n''pip install roner''\n\n... | [
"TAGS\n#transformers #pytorch #bert #token-classification #ro #dataset-ronec #arxiv-1909.01247 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThere are 2 ways to use this model:",
"#### Directly in Transformers:\n\n\nYou can use this model with Transform... |
fill-mask | transformers |
# bert-base-romanian-uncased-v1
The BERT **base**, **uncased** model for Romanian, trained on a 15GB corpus, version 
### How to use
```python
from transformers import AutoTokenizer, AutoModel
import torch
# load tokenizer and model
tokenizer = AutoT... | {"language": "ro", "license": "mit", "tags": ["bert", "fill-mask"]} | dumitrescustefan/bert-base-romanian-uncased-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ro",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #ro #license-mit #endpoints_compatible #region-us
| bert-base-romanian-uncased-v1
=============================
The BERT base, uncased model for Romanian, trained on a 15GB corpus, version !v1.0
### How to use
Remember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :
because the model was NOT trained on cedilla ''s'' ... | [
"### How to use\n\n\nRemember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :\n\n\nbecause the model was NOT trained on cedilla ''s'' and ''t''s. If you don't, you will have decreased performance due to ''''s and increased number of tokens per word.",
"### Evaluation\... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ro #license-mit #endpoints_compatible #region-us \n",
"### How to use\n\n\nRemember to always sanitize your text! Replace ''s'' and ''t'' cedilla-letters to comma-letters with :\n\n\nbecause the model was NOT trained on cedilla ''s'' and ''t''s. If you don't, y... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Lithuanian 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 mode... | {"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Lithuanian by Enes Burak Dundar", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | dundar/wav2vec2-large-xlsr-53-lithuanian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian 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 evaluate... | [
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian 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... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using t... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish 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": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Enes Burak Dundar", "results": [{"task": {"type": "automatic-speech-recognition", "name":... | dundar/wav2vec2-large-xlsr-53-turkish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish 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 f... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish 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 #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Com... |
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. -->
# indic-transformers-te-distilbert
This model was trained from scratch on the wikiann dataset.
It achieves the following results o... | {"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "indic-transformers-te-distilbert", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args"... | durgaamma2005/indic-transformers-te-distilbert | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-wikiann #model-index #autotrain_compatible #endpoints_compatible #region-us
| indic-transformers-te-distilbert
================================
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2940
* Precision: 0.5657
* Recall: 0.6486
* F1: 0.6043
* Accuracy: 0.9049
Model description
-----------------
More info... | [
"### 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 #distilbert #token-classification #generated_from_trainer #dataset-wikiann #model-index #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... |
fill-mask | transformers |
# Bertinho-gl-base-cased
A pre-trained BERT model for Galician (12layers, cased). Trained on Wikipedia
| {"language": "gl", "widget": [{"text": "As filloas son un [MASK] t\u00edpico do entroido en Galicia "}]} | dvilares/bertinho-gl-base-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"gl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gl"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #gl #autotrain_compatible #endpoints_compatible #region-us
|
# Bertinho-gl-base-cased
A pre-trained BERT model for Galician (12layers, cased). Trained on Wikipedia
| [
"# Bertinho-gl-base-cased\n\nA pre-trained BERT model for Galician (12layers, cased). Trained on Wikipedia"
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #gl #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bertinho-gl-base-cased\n\nA pre-trained BERT model for Galician (12layers, cased). Trained on Wikipedia"
] |
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