pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers | # 🔑 Keyphrase Extraction Model: distilbert-openkp
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first... | {"language": "en", "license": "mit", "tags": ["keyphrase-extraction"], "datasets": ["midas/openkp"], "metrics": ["seqeval"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content ... | ml6team/keyphrase-extraction-distilbert-openkp | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/openkp",
"arxiv:1911.02671",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-25T10:16:40+00:00 | [
"1911.02671"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #keyphrase-extraction #en #dataset-midas/openkp #arxiv-1911.02671 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| Keyphrase Extraction Model: distilbert-openkp
=============================================
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it... | [
"### Limitations\n\n\n* Limited amount of predicted keyphrases.\n* Only works for English documents.",
"### How To Use\n\n\nTraining Dataset\n----------------\n\n\nOpenKP is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated k... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #keyphrase-extraction #en #dataset-midas/openkp #arxiv-1911.02671 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Limitations\n\n\n* Limited amount of predicted keyphrases.\n* Only works for Engli... |
token-classification | flair |
## Persian NER in Flair
This is the universal Named-entity recognition model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **84.03** (NSURL-2019)
Predicts NER tags:
| **tag** | **meaning** |
|:---------------------------------:|:-----------:|
| PER ... | {"language": "fa", "tags": ["flair", "token-classification", "sequence-tagger-model"], "dataset": ["NSURL-2019"], "widget": [{"text": "\u0622\u062e\u0631\u06cc\u0646 \u0645\u0642\u0627\u0645 \u0628\u0631\u062c\u0633\u062a\u0647 \u0698\u0627\u067e\u0646\u06cc \u06a9\u0647 \u067e\u0633 \u0627\u0632 \u0627\u0646\u0642\u06... | hamedkhaledi/persain-flair-ner | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fa",
"region:us"
] | null | 2022-03-25T10:16:41+00:00 | [] | [
"fa"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fa #region-us
| Persian NER in Flair
--------------------
This is the universal Named-entity recognition model for Persian that ships with Flair.
F1-Score: 84.03 (NSURL-2019)
Predicts NER tags:
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
---
### Resul... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\n\n\n---",
"### Results\n\n\n* F-score (micro) 0.8403\n* F-score (macro) 0.8656\n* Accuracy 0.7357"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fa #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\n\n\n---",
"### Results\n\n\n* F-score (micro) 0.8403\n* F-score (macro) 0.8656\n* Accuracy 0.7357"
] |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-dtm-domain-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在 OPPO 手机助手小布对话匹配数据集（[BUSTM](https://github.com/xiaobu-coai/BUSTM)）上进行训练调优，适用于**开放领域的对话匹配**场景（偏口语化），比如：
- 哪有好玩的 VS. 这附近有什么好玩的地方
- 定时25分钟 VS. 计时半个小时
- 我要听王琦的歌 VS. 放一首王琦的歌
注：此模型的[轻量化版本](https://huggi... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-dtm-domain-v1 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T10:18:38+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-dtm-domain-v1
=====================================
此模型基于 bert-base-chinese 版本 BERT 模型，在 OPPO 手机助手小布对话匹配数据集（BUSTM）上进行训练调优，适用于开放领域的对话匹配场景（偏口语化），比如：
* 哪有好玩的 VS. 这附近有什么好玩的地方
* 定时25分钟 VS. 计时半个小时
* 我要听王琦的歌 VS. 放一首王琦的歌
注：此模型的轻量化版本，也已经开源啦！
Usage
=====
1. Sentence-Transformers
----------------... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# DMetaSoul/sbert-chinese-qmc-finance-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在大规模银行问题匹配数据集（[BQCorpus](http://icrc.hitsz.edu.cn/info/1037/1162.htm)）上进行训练调优，适用于**金融领域的问题匹配**场景，比如：
- 8千日利息400元? VS 10000元日利息多少钱
- 提前还款是按全额计息 VS 还款扣款不成功怎么还款？
- 为什么我借钱交易失败 VS 刚申请的借款为什么会失败
注：此模型的[轻... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "semantic-search", "chinese"], "pipeline_tag": "sentence-similarity"} | DMetaSoul/sbert-chinese-qmc-finance-v1 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T10:23:55+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us
| DMetaSoul/sbert-chinese-qmc-finance-v1
======================================
此模型基于 bert-base-chinese 版本 BERT 模型，在大规模银行问题匹配数据集（BQCorpus）上进行训练调优，适用于金融领域的问题匹配场景，比如：
* 8千日利息400元? VS 10000元日利息多少钱
* 提前还款是按全额计息 VS 还款扣款不成功怎么还款？
* 为什么我借钱交易失败 VS 刚申请的借款为什么会失败
注：此模型的轻量化版本，也已经开源啦！
Usage
=====
1. Sentence-Transformers
---... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #semantic-search #chinese #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_augment_0.1.csv
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_augment_0.1.csv", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T11:09:40+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_augment\_0.1.csv
=================================================
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: 2.3933
* Wer: 0.9997
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
text-generation | transformers |
## HingGPT-Devanagari
HingGPT-Devanagari is a Hindi-English code-mixed GPT model trained on Devanagari text. It is a GPT2 model trained on L3Cube-HingCorpus.
<br>
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
More details on the dataset, models, and baseline results can be found in our [paper] (https... | {"language": ["hi", "en", "multilingual"], "license": "cc-by-4.0", "tags": ["hi", "en", "codemix"], "datasets": ["L3Cube-HingCorpus"]} | l3cube-pune/hing-gpt-devanagari | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"hi",
"en",
"codemix",
"multilingual",
"dataset:L3Cube-HingCorpus",
"arxiv:2204.08398",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T11:39:00+00:00 | [
"2204.08398"
] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## HingGPT-Devanagari
HingGPT-Devanagari is a Hindi-English code-mixed GPT model trained on Devanagari text. It is a GPT2 model trained on L3Cube-HingCorpus.
<br>
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Other models from HingBERT family: <br>
<a ... | [
"## HingGPT-Devanagari\nHingGPT-Devanagari is a Hindi-English code-mixed GPT model trained on Devanagari text. It is a GPT2 model trained on L3Cube-HingCorpus.\n<br>\n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\nOther models from HingBERT famil... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## HingGPT-Devanagari\nHingGPT-Devanagari is a Hindi-English code... |
null | transformers |
# wav2vec2-base-cs-80k-ClTRUS
**C**zech **l**anguage **TR**ransformer from **U**nlabeled **S**peech (ClTRUS) is a monolingual Czech Wav2Vec 2.0 base model pre-trained from 80 thousand hours of Czech speech.
This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech... | {"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["Czech", "KKY", "FAV"]} | fav-kky/wav2vec2-base-cs-80k-ClTRUS | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"Czech",
"KKY",
"FAV",
"cs",
"arxiv:2206.07627",
"arxiv:2206.07666",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T11:45:13+00:00 | [
"2206.07627",
"2206.07666"
] | [
"cs"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #Czech #KKY #FAV #cs #arxiv-2206.07627 #arxiv-2206.07666 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# wav2vec2-base-cs-80k-ClTRUS
Czech language TRransformer from Unlabeled Speech (ClTRUS) is a monolingual Czech Wav2Vec 2.0 base model pre-trained from 80 thousand hours of Czech speech.
This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a toke... | [
"# wav2vec2-base-cs-80k-ClTRUS\nCzech language TRransformer from Unlabeled Speech (ClTRUS) is a monolingual Czech Wav2Vec 2.0 base model pre-trained from 80 thousand hours of Czech speech.\n\nThis model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #Czech #KKY #FAV #cs #arxiv-2206.07627 #arxiv-2206.07666 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-cs-80k-ClTRUS\nCzech language TRransformer from Unlabeled Speech (ClTRUS) is a monolingual Czech Wav2Vec 2.0 base model pre-... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv
This model is a fine-tuned version of [facebook/wav2vec2-base](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T11:45:23+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data\_augment\_0.1.csv
==========================================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4695
* Wer: 1.0
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 667919695
- CO2 Emissions (in grams): 207.64739623144084
## Validation Metrics
- Loss: 0.06461456418037415
- Rouge1: 70.5184
- Rouge2: 66.9204
- RougeL: 70.4464
- RougeLsum: 70.4705
- Gen Len: 18.5385
## Usage
You can use cURL to access thi... | {"language": "unk", "tags": "autotrain", "datasets": ["McIan91/autotrain-data-parrot_finetune_v1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 207.64739623144084} | ianMconversica/autotrain-parrot_finetune_v1-667919695 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:McIan91/autotrain-data-parrot_finetune_v1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T12:27:52+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-McIan91/autotrain-data-parrot_finetune_v1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 667919695
- CO2 Emissions (in grams): 207.64739623144084
## Validation Metrics
- Loss: 0.06461456418037415
- Rouge1: 70.5184
- Rouge2: 66.9204
- RougeL: 70.4464
- RougeLsum: 70.4705
- Gen Len: 18.5385
## Usage
You can use cURL to access thi... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 667919695\n- CO2 Emissions (in grams): 207.64739623144084",
"## Validation Metrics\n\n- Loss: 0.06461456418037415\n- Rouge1: 70.5184\n- Rouge2: 66.9204\n- RougeL: 70.4464\n- RougeLsum: 70.4705\n- Gen Len: 18.5385",
"## Usage\n\nYou c... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-McIan91/autotrain-data-parrot_finetune_v1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 6679196... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-albert-base-v2-imdb
This model is a fine-tuned version of [textattack/albert-base-v2-imdb](https://huggingface.co/textattack/albert... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-albert-base-v2-imdb", "results": []}]} | vumichien/albert-base-v2-imdb | null | [
"transformers",
"tf",
"albert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T12:32:02+00:00 | [] | [] | TAGS
#transformers #tf #albert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tf-albert-base-v2-imdb
This model is a fine-tuned version of textattack/albert-base-v2-imdb on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mor... | [
"# tf-albert-base-v2-imdb\n\nThis model is a fine-tuned version of textattack/albert-base-v2-imdb on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #tf #albert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# tf-albert-base-v2-imdb\n\nThis model is a fine-tuned version of textattack/albert-base-v2-imdb on an unknown dataset.\nIt achieves the following results on the evalua... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-albert-base-v2
This model is a fine-tuned version of [vumichien/albert-base-v2](https://huggingface.co/vumichien/albert-base-v2) on... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-albert-base-v2", "results": []}]} | vumichien/tf-albert-base-v2 | null | [
"transformers",
"tf",
"albert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T12:42:00+00:00 | [] | [] | TAGS
#transformers #tf #albert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tf-albert-base-v2
This model is a fine-tuned version of vumichien/albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informati... | [
"# tf-albert-base-v2\n\nThis model is a fine-tuned version of vumichien/albert-base-v2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation ... | [
"TAGS\n#transformers #tf #albert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# tf-albert-base-v2\n\nThis model is a fine-tuned version of vumichien/albert-base-v2 on an unknown dataset.\nIt achieves the following results on the evaluation set:"... |
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. -->
# mbert-finnic-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multiling... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mbert-finnic-ner", "results": []}]} | azizbarank/mbert-finnic-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T12:43:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mbert-finnic-ner
================
This model is a fine-tuned version of bert-base-multilingual-cased on the Finnish and Estonian parts of the "WikiANN" dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1427
* Precision: 0.9090
* Recall: 0.9156
* F1: 0.9123
* Accuracy: 0.9672
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #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\... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-albert-base-v2-squad2
This model is a fine-tuned version of [twmkn9/albert-base-v2-squad2](https://huggingface.co/twmkn9/albert-bas... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-albert-base-v2-squad2", "results": []}]} | vumichien/albert-base-v2-squad2 | null | [
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T12:48:04+00:00 | [] | [] | TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #endpoints_compatible #region-us
|
# tf-albert-base-v2-squad2
This model is a fine-tuned version of twmkn9/albert-base-v2-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mor... | [
"# tf-albert-base-v2-squad2\n\nThis model is a fine-tuned version of twmkn9/albert-base-v2-squad2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and ... | [
"TAGS\n#transformers #tf #albert #question-answering #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# tf-albert-base-v2-squad2\n\nThis model is a fine-tuned version of twmkn9/albert-base-v2-squad2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-emo-mobilebert
This model is a fine-tuned version of [lordtt13/emo-mobilebert](https://huggingface.co/lordtt13/emo-mobilebert) on a... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-emo-mobilebert", "results": []}]} | vumichien/emo-mobilebert | null | [
"transformers",
"tf",
"mobilebert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T12:59:51+00:00 | [] | [] | TAGS
#transformers #tf #mobilebert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# tf-emo-mobilebert
This model is a fine-tuned version of lordtt13/emo-mobilebert on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informatio... | [
"# tf-emo-mobilebert\n\nThis model is a fine-tuned version of lordtt13/emo-mobilebert on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation d... | [
"TAGS\n#transformers #tf #mobilebert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# tf-emo-mobilebert\n\nThis model is a fine-tuned version of lordtt13/emo-mobilebert on an unknown dataset.\nIt achieves the following results on the evaluation set... |
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. -->
# pegasus-xsum-new-dataset
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum)... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-xsum-new-dataset", "results": []}]} | ssardorf/pegasus-xsum-new-dataset | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T13:07:00+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-xsum-new-dataset
This model is a fine-tuned version of google/pegasus-xsum on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8355
- Rouge1: 48.7306
- Rouge2: 34.1291
- Rougel: 44.0778
- Rougelsum: 45.7139
- Gen Len: 30.8889
## Model description
More information ne... | [
"# pegasus-xsum-new-dataset\n\nThis model is a fine-tuned version of google/pegasus-xsum on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8355\n- Rouge1: 48.7306\n- Rouge2: 34.1291\n- Rougel: 44.0778\n- Rougelsum: 45.7139\n- Gen Len: 30.8889",
"## Model description\n\nMo... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-xsum-new-dataset\n\nThis model is a fine-tuned version of google/pegasus-xsum on an unknown dataset.\nIt achieves the following results on the evaluation set:... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-mobilebert-uncased-squad-v2
This model is a fine-tuned version of [csarron/mobilebert-uncased-squad-v2](https://huggingface.co/csar... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-mobilebert-uncased-squad-v2", "results": []}]} | vumichien/mobilebert-uncased-squad-v2 | null | [
"transformers",
"tf",
"mobilebert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T13:07:29+00:00 | [] | [] | TAGS
#transformers #tf #mobilebert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
|
# tf-mobilebert-uncased-squad-v2
This model is a fine-tuned version of csarron/mobilebert-uncased-squad-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluat... | [
"# tf-mobilebert-uncased-squad-v2\n\nThis model is a fine-tuned version of csarron/mobilebert-uncased-squad-v2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #tf #mobilebert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"# tf-mobilebert-uncased-squad-v2\n\nThis model is a fine-tuned version of csarron/mobilebert-uncased-squad-v2 on an unknown dataset.\nIt achieves the following results on the... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-mobilebert-finetuned-ner
This model is a fine-tuned version of [mrm8488/mobilebert-finetuned-ner](https://huggingface.co/mrm8488/mo... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-mobilebert-finetuned-ner", "results": []}]} | vumichien/mobilebert-finetuned-ner | null | [
"transformers",
"tf",
"mobilebert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T13:12:31+00:00 | [] | [] | TAGS
#transformers #tf #mobilebert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# tf-mobilebert-finetuned-ner
This model is a fine-tuned version of mrm8488/mobilebert-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation da... | [
"# tf-mobilebert-finetuned-ner\n\nThis model is a fine-tuned version of mrm8488/mobilebert-finetuned-ner on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Traini... | [
"TAGS\n#transformers #tf #mobilebert #token-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# tf-mobilebert-finetuned-ner\n\nThis model is a fine-tuned version of mrm8488/mobilebert-finetuned-ner on an unknown dataset.\nIt achieves the follow... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# try-m-e-perplexity594
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "try-m-e-perplexity594", "results": []}]} | bigmorning/try-m-e-perplexity594 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T13:28:27+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# try-m-e-perplexity594
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# try-m-e-perplexity594\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMo... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# try-m-e-perplexity594\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following r... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tf-bert-base-cased-squad2
This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-b... | {"license": "cc-by-4.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tf-bert-base-cased-squad2", "results": []}]} | vumichien/tf-bert-base-cased-squad2 | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T13:56:15+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us
|
# tf-bert-base-cased-squad2
This model is a fine-tuned version of deepset/bert-base-cased-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
... | [
"# tf-bert-base-cased-squad2\n\nThis model is a fine-tuned version of deepset/bert-base-cased-squad2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training a... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# tf-bert-base-cased-squad2\n\nThis model is a fine-tuned version of deepset/bert-base-cased-squad2 on an unknown dataset.\nIt achieves the following results on the evaluatio... |
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_toy_train_data_augment_0.1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_augment_0.1", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_augment_0.1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T14:40:37+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_augment\_0.1
=============================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3786
* Wer: 0.9954
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1421403684085374979/SoqY... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rivatez/1648220244511/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rivatez | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T14:51:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Riva
@rivatez
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/temp-colab-upload-test4
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Rocketknight1/temp-colab-upload-test4", "results": []}]} | Rocketknight1/temp-colab-upload-test4 | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T15:06:07+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Rocketknight1/temp-colab-upload-test4
=====================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0000
* Validation Loss: 0.0000
* Epoch: 1
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'learning\\_rate':... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Wende/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T15:21:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0575
* Precision: 0.9322
* Recall: 0.9505
* F1: 0.9413
* Accuracy: 0.9860
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
feature-extraction | transformers |
# MQDD - Multimodal Question Duplicity Detection
This repository publishes pre-trained model for the paper
[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
The Stack Overflow Datasets (SOD) an... | {"license": "cc-by-nc-sa-4.0"} | UWB-AIR/MQDD-pretrained | null | [
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2203.14093",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T16:16:40+00:00 | [
"2203.14093"
] | [] | TAGS
#transformers #pytorch #longformer #feature-extraction #arxiv-2203.14093 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# MQDD - Multimodal Question Duplicity Detection
This repository publishes pre-trained model for the paper
MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain. For more information, see the paper.
The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (... | [
"# MQDD - Multimodal Question Duplicity Detection\r\n\r\nThis repository publishes pre-trained model for the paper \r\nMQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain. For more information, see the paper.\r\nThe Stack Overflow Datasets (SOD) and Stack Overflow Duplicit... | [
"TAGS\n#transformers #pytorch #longformer #feature-extraction #arxiv-2203.14093 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# MQDD - Multimodal Question Duplicity Detection\r\n\r\nThis repository publishes pre-trained model for the paper \r\nMQDD – Pre-training of Multimodal Question Duplicity... |
feature-extraction | transformers |
# MQDD - Multimodal Question Duplicity Detection
This repository publishes trained models and other supporting materials for the paper
[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
The Stac... | {"license": "cc-by-nc-sa-4.0"} | UWB-AIR/MQDD-duplicates | null | [
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2203.14093",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T16:17:08+00:00 | [
"2203.14093"
] | [] | TAGS
#transformers #pytorch #longformer #feature-extraction #arxiv-2203.14093 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# MQDD - Multimodal Question Duplicity Detection
This repository publishes trained models and other supporting materials for the paper
MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain. For more information, see the paper.
The Stack Overflow Datasets (SOD) and Stack ... | [
"# MQDD - Multimodal Question Duplicity Detection\r\n\r\nThis repository publishes trained models and other supporting materials for the paper \r\nMQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain. For more information, see the paper.\r\nThe Stack Overflow Datasets (SOD)... | [
"TAGS\n#transformers #pytorch #longformer #feature-extraction #arxiv-2203.14093 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# MQDD - Multimodal Question Duplicity Detection\r\n\r\nThis repository publishes trained models and other supporting materials for the paper \r\nMQDD – Pre-training of M... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data_augment_0.1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data_augment_0.1", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T17:45:52+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data\_augment\_0.1
======================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4658
* Wer: 0.5037
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
fill-mask | transformers |
## HingBERT-Mixed
HingBERT-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a base BERT model fine-tuned on mixed script L3Cube-HingCorpus.
<br>
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
More details on the dataset, models, and baseline results can be found... | {"language": ["hi", "en", "multilingual"], "license": "cc-by-4.0", "tags": ["hi", "en", "codemix"], "datasets": ["L3Cube-HingCorpus"]} | l3cube-pune/hing-mbert-mixed | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"hi",
"en",
"codemix",
"multilingual",
"dataset:L3Cube-HingCorpus",
"arxiv:2204.08398",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T17:52:23+00:00 | [
"2204.08398"
] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HingBERT-Mixed
HingBERT-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a base BERT model fine-tuned on mixed script L3Cube-HingCorpus.
<br>
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Other models from Hin... | [
"## HingBERT-Mixed\nHingBERT-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a base BERT model fine-tuned on mixed script L3Cube-HingCorpus.\n<br>\n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\nOther mode... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HingBERT-Mixed\nHingBERT-Mixed is a Hindi-English code-mixed BERT model trained on roman + deva... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pinecone/msmarco-distilbert-base-tas-b-covid | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-25T18:20:41+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1504530325526900756/QOTZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/huggingpuppy/1648233768787/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/huggingpuppy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T18:41:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
hug. (INGROUP INTERN)
@huggingpuppy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training da... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | 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. -->
# mt5-finetuned-en-ar
This model is a fine-tuned version of [ahmeddbahaa/mt5-small-finetuned-mt5-en](https://huggingface.co/ahmedd... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-finetuned-en-ar", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type": "xlsum... | ahmeddbahaa/mt5-finetuned-en-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T19:26:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-finetuned-en-ar
===================
This model is a fine-tuned version of ahmeddbahaa/mt5-small-finetuned-mt5-en on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2314
* Rouge1: 0.2824
* Rouge2: 0.0
* Rougel: 0.2902
* Rougelsum: 0.298
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
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. -->
# reversed_harrypotter_generation
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the Non... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reversed_harrypotter_generation", "results": []}]} | calebcsjm/reversed_harrypotter_generation | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T20:58:10+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
|
# reversed_harrypotter_generation
This model is a fine-tuned version of distilgpt2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamete... | [
"# reversed_harrypotter_generation\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",... | [
"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",
"# reversed_harrypotter_generation\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## M... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374075536595505154/1_1j... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/_stevenshoe-mkobach/1648247026634/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/_stevenshoe-mkobach | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-25T22:08:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Matthew Kobach & Steven Shoemaker
@\_stevenshoe-mkobach
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# donyd/distilbert-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "donyd/distilbert-finetuned-imdb", "results": []}]} | donyd/distilbert-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T00:32:31+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| donyd/distilbert-finetuned-imdb
===============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8432
* Validation Loss: 2.6247
* Epoch: 0
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 670319725
- CO2 Emissions (in grams): 149.95517950000834
## Validation Metrics
- Loss: 0.0022294693626463413
- Rouge1: 67.5833
- Rouge2: 65.7386
- RougeL: 67.5812
- RougeLsum: 67.585
- Gen Len: 18.907
## Usage
You can use cURL to access thi... | {"language": "unk", "tags": "autotrain", "datasets": ["McIan91/autotrain-data-phrasinator-reverse"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 149.95517950000834} | ianMconversica/autotrain-phrasinator-reverse-670319725 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:McIan91/autotrain-data-phrasinator-reverse",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-26T01:38:37+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-McIan91/autotrain-data-phrasinator-reverse #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 670319725
- CO2 Emissions (in grams): 149.95517950000834
## Validation Metrics
- Loss: 0.0022294693626463413
- Rouge1: 67.5833
- Rouge2: 65.7386
- RougeL: 67.5812
- RougeLsum: 67.585
- Gen Len: 18.907
## Usage
You can use cURL to access thi... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 670319725\n- CO2 Emissions (in grams): 149.95517950000834",
"## Validation Metrics\n\n- Loss: 0.0022294693626463413\n- Rouge1: 67.5833\n- Rouge2: 65.7386\n- RougeL: 67.5812\n- RougeLsum: 67.585\n- Gen Len: 18.907",
"## Usage\n\nYou c... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-McIan91/autotrain-data-phrasinator-reverse #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 670319... |
text-generation | transformers |
# GPT-2 Small Portuguese Lyrics
Pretrained model from lyrics dataset in Portuguese.
## Model description
The model was trained from a Kaggle Dataset, [“Song lyrics from 6 musical genres”](https://www.kaggle.com/neisse/scrapped-lyrics-from-6-genres/version/2), with around 66,000 songs in portuguese.
The model was f... | {"language": "pt", "license": "mit"} | rsmonteiro/gpt2-small-portuguese-lyrics | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"pt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-26T02:02:49+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #tensorboard #gpt2 #text-generation #pt #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2 Small Portuguese Lyrics
=============================
Pretrained model from lyrics dataset in Portuguese.
Model description
-----------------
The model was trained from a Kaggle Dataset, “Song lyrics from 6 musical genres”, with around 66,000 songs in portuguese.
The model was fine-tuned from GPorTuguese-... | [] | [
"TAGS\n#transformers #pytorch #tf #tensorboard #gpt2 #text-generation #pt #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# try_tpu_distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "try_tpu_distilbert", "results": []}]} | bigmorning/try_tpu_distilbert | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T03:25:38+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# try_tpu_distilbert
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informati... | [
"# try_tpu_distilbert\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation ... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# try_tpu_distilbert\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the eva... |
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-small-finetuned-multi-news
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_new... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-multi-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "mu... | nikhedward/t5-small-finetuned-multi-news | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-26T03:43:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-multi-news
=============================
This model is a fine-tuned version of t5-small on the multi\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7775
* Rouge1: 14.5549
* Rouge2: 4.5934
* Rougel: 11.1178
* Rougelsum: 12.8964
* Gen Len: 19.0
Model description... | [
"### 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* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# albert_squad_2.0
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "albert_squad_2.0", "results": []}]} | SS8/albert_squad_2.0 | null | [
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T03:52:02+00:00 | [] | [] | TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| albert\_squad\_2.0
==================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6320
* Validation Loss: 0.8542
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'LAMB', 'learning\\_rate': 3e-05, 'weight\\_decay': 0.0, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-06, 'exclude\\_from\\_weight\\_decay': None, 'exclude\\_from\\_layer\\_adapt... | [
"TAGS\n#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'LAMB', 'learning\\_rate': 3e-05, 'weight\\_decay': 0... |
fill-mask | transformers |
## HingRoBERTa-Mixed
HingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a xlm-RoBERTa model fine-tuned on mixed script L3Cube-HingCorpus.
<br>
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
More details on the dataset, models, and baseline results can ... | {"language": ["hi", "en", "multilingual"], "license": "cc-by-4.0", "tags": ["hi", "en", "codemix"], "datasets": ["L3Cube-HingCorpus"]} | l3cube-pune/hing-roberta-mixed | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"hi",
"en",
"codemix",
"multilingual",
"dataset:L3Cube-HingCorpus",
"arxiv:2204.08398",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T06:15:28+00:00 | [
"2204.08398"
] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## HingRoBERTa-Mixed
HingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a xlm-RoBERTa model fine-tuned on mixed script L3Cube-HingCorpus.
<br>
[dataset link] (URL
More details on the dataset, models, and baseline results can be found in our [paper] (URL
Other models ... | [
"## HingRoBERTa-Mixed\nHingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a xlm-RoBERTa model fine-tuned on mixed script L3Cube-HingCorpus.\n<br>\n[dataset link] (URL\n\nMore details on the dataset, models, and baseline results can be found in our [paper] (URL\n\nOt... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #hi #en #codemix #multilingual #dataset-L3Cube-HingCorpus #arxiv-2204.08398 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## HingRoBERTa-Mixed\nHingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on... |
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... | jasonyim2/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T06:45:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2227
* Accuracy: 0.9215
* F1: 0.9215
Model description
-----------------
Mo... | [
"### 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 #tensorboard #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* learn... |
null | null | A dataset trained on known dialogue from AM in Harlan Ellison's video game adaption of "I have no mouth and I must scream," alongside the initial quote about hate. Model historically uses DialoGPT, however, will be updated and/or converted to C1-6B as soon as possible. | {"license": "other"} | BowmanFox/AlliedMasterComputer | null | [
"license:other",
"region:us"
] | null | 2022-03-26T07:14:49+00:00 | [] | [] | TAGS
#license-other #region-us
| A dataset trained on known dialogue from AM in Harlan Ellison's video game adaption of "I have no mouth and I must scream," alongside the initial quote about hate. Model historically uses DialoGPT, however, will be updated and/or converted to C1-6B as soon as possible. | [] | [
"TAGS\n#license-other #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_augmented
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_augmented", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_augmented | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T07:36:21+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_augmented
==========================================
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: 1.0238
* Wer: 0.6969
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
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_test
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2_test", "results": []}]} | snehatyagi/wav2vec2_test | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T09:11:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_test
==============
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: 91.1661
* Wer: 0.5714
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: 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* 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: 8... |
text-generation | transformers |
# Transformers4IME
Transformers4IME is repo for exploring and adapting transformer-based models to IME.
## PinyinGPT
PinyinGPT is a model from [Exploring and Adapting Chinese GPT to Pinyin Input Meth... | {"license": "cc-by-nc-sa-4.0"} | aihijo/transformers4ime-pinyingpt-concat | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2203.00249",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-26T09:54:26+00:00 | [
"2203.00249"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2203.00249 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
!ime
# Transformers4IME
Transformers4IME is repo for exploring and adapting transformer-based models to IME.
## PinyinGPT
PinyinGPT is a model from Exploring and Adapting Chinese GPT to Pinyin Input Method
which appears in ACL2022.
The code can be found at
* Gitee
* Github
| [
"# Transformers4IME\r\n\r\nTransformers4IME is repo for exploring and adapting transformer-based models to IME.",
"## PinyinGPT\r\n\r\nPinyinGPT is a model from Exploring and Adapting Chinese GPT to Pinyin Input Method \r\nwhich appears in ACL2022.\r\n\r\n\r\nThe code can be found at \r\n* Gitee\r\n* Github"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2203.00249 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Transformers4IME\r\n\r\nTransformers4IME is repo for exploring and adapting transformer-based models to IME.",
"## PinyinGPT\r... |
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_toy_train_data_fast_10pct
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_fast_10pct", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_fast_10pct | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T10:09:45+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_fast\_10pct
============================================
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: 1.3087
* Wer: 0.7175
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
fill-mask | transformers |
# hmBERT: Historical Multilingual Language Models for Named Entity Recognition
More information about our hmBERT model can be found in our new paper:
["hmBERT: Historical Multilingual Language Models for Named Entity Recognition"](https://arxiv.org/abs/2205.15575).
## Languages
Our Historic Language Models Zoo cont... | {"language": "multilingual", "license": "mit", "widget": [{"text": "and I cannot conceive the reafon why [MASK] hath"}, {"text": "T\u00e4k\u00e4l\u00e4inen sanomalehdist\u00f6 [MASK] erit - t\u00e4in"}, {"text": "Det vore [MASK] h\u00e4ller n\u00f6dv\u00e4ndigt att be"}, {"text": "Comme, \u00e0 cette \u00e9poque [MASK]... | dbmdz/bert-base-historic-multilingual-64k-td-cased | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"arxiv:2205.15575",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T11:52:36+00:00 | [
"2205.15575"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #onnx #safetensors #bert #fill-mask #multilingual #arxiv-2205.15575 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| hmBERT: Historical Multilingual Language Models for Named Entity Recognition
============================================================================
More information about our hmBERT model can be found in our new paper:
"hmBERT: Historical Multilingual Language Models for Named Entity Recognition".
Languages
-... | [] | [
"TAGS\n#transformers #pytorch #onnx #safetensors #bert #fill-mask #multilingual #arxiv-2205.15575 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_masked_audio_10ms
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_masked_audio_10ms", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_masked_audio_10ms | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T12:28:41+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_masked\_audio\_10ms
====================================================
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: 1.2477
* Wer: 0.7145
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "xlnet-base-cased", "results": []}]} | Mr-Wick/xlnet-base-cased | null | [
"transformers",
"tf",
"xlnet",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T12:52:07+00:00 | [] | [] | TAGS
#transformers #tf #xlnet #question-answering #generated_from_keras_callback #endpoints_compatible #region-us
|
# xlnet-base-cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# xlnet-base-cased\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #tf #xlnet #question-answering #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# xlnet-base-cased\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed"... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Graphcore/lxmert-gqa-uncased
BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is des... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Graphcore/gqa-lxmert"], "metrics": ["accuracy"], "model-index": [{"name": "gqa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "Graphcore/gqa-lxmert", "type": "Graphcore/gqa-lxmert", "arg... | Graphcore/lxmert-gqa-uncased | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"lxmert",
"question-answering",
"generated_from_trainer",
"dataset:Graphcore/gqa-lxmert",
"arxiv:1908.07490",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T13:05:46+00:00 | [
"1908.07490"
] | [] | TAGS
#transformers #pytorch #optimum_graphcore #lxmert #question-answering #generated_from_trainer #dataset-Graphcore/gqa-lxmert #arxiv-1908.07490 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Graphcore/lxmert-gqa-uncased
BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity Rec... | [
"# Graphcore/lxmert-gqa-uncased\n\nBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #lxmert #question-answering #generated_from_trainer #dataset-Graphcore/gqa-lxmert #arxiv-1908.07490 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Graphcore/lxmert-gqa-uncased\n\nBERT (Bidirectional Encoder Representations from Transforme... |
null | null | | | uid | hidden_size |
|---:|:------------------------------------------------------------------------------------------------------------------------|--------------:|
| 0 | [e87a4e028b11ec7bf770c6... | {} | zuppif/versioning-test | null | [
"region:us"
] | null | 2022-03-26T13:34:47+00:00 | [] | [] | TAGS
#region-us
| [] | [
"TAGS\n#region-us \n"
] | |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert500e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert500e", "results": []}]} | bigmorning/distilbert500e | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T14:48:24+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert500e
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information n... | [
"# distilbert500e\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert500e\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluat... |
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_toy_train_data_masked_audio
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_masked_audio", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_masked_audio | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T14:57:40+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_masked\_audio
==============================================
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: 1.1950
* Wer: 0.7340
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #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: 16\n* eval\\_b... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert1000e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert1000e", "results": []}]} | bigmorning/distilbert1000e | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T15:27:21+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert1000e
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information ... | [
"# distilbert1000e\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation dat... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert1000e\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evalua... |
image-classification | transformers |
# CarViT
CarViT - Car make classifier that can identify 40 manufacturers.
## Example Images
#### Acura
#### Alfa Romeo
#### Aston Martin
#### Audi
#### BMW
![BMW](images/BMW... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | abdusah/CarViT | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-26T16:04:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CarViT
CarViT - Car make classifier that can identify 40 manufacturers.
## Example Images
#### Acura
!Acura
#### Alfa Romeo
!Alfa Romeo
#### Aston Martin
!Aston Martin
#### Audi
!Audi
#### BMW
!BMW
#### Bentley
!Bentley
#### Buick
!Buick
#### Cadillac
!Cadillac
#### Chevrolet
!Chevrolet
####... | [
"# CarViT\n\n \nCarViT - Car make classifier that can identify 40 manufacturers.",
"## Example Images",
"#### Acura\n\n!Acura",
"#### Alfa Romeo\n\n!Alfa Romeo",
"#### Aston Martin\n\n!Aston Martin",
"#### Audi\n\n!Audi",
"#### BMW\n\n!BMW",
"#### Bentley\n\n!Bentley",
"#### Buick\n\n!Buick",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CarViT\n\n \nCarViT - Car make classifier that can identify 40 manufacturers.",
"## Example Images",
"#### Acura\n\n!Acura",
... |
null | null | The main objective of this project is to create a chatbot that could be used as an application for stress relief for people who feel lonely and need emotional support. This project mainly focuses on creating an environment for people who are distressed and need a companion to talk or chat with.
Our main objective is t... | {} | kchanakya91/happychatbot | null | [
"region:us"
] | null | 2022-03-26T16:29:24+00:00 | [] | [] | TAGS
#region-us
| The main objective of this project is to create a chatbot that could be used as an application for stress relief for people who feel lonely and need emotional support. This project mainly focuses on creating an environment for people who are distressed and need a companion to talk or chat with.
Our main objective is t... | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt2-500e
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achiev... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt2-500e", "results": []}]} | bigmorning/distilgpt2-500e | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-26T16:31:57+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-500e
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# distilgpt2-500e\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-500e\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.\nIt achieves the following results... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-bart-large-cnn-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T17:08:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9938
* Wer: 0.9745
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
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. -->
# electricidad-small-discriminator-finetuned-clasificacion-texto-suicida
This model is a fine-tuned version of [mrm8488/electricid... | {"language": "es", "license": "afl-3.0", "tags": ["generated_from_trainer", "sentiment", "emotion"], "metrics": ["accuracy"], "widget": [{"text": "La vida no merece la pena", "example_title": "Ejemplo 1"}, {"text": "Para vivir as\u00ed lo mejor es estar muerto", "example_title": "Ejemplo 2"}, {"text": "me siento triste... | dannyvas23/electricidad-small-discriminator-finetuned-clasificacion-texto-suicida | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"sentiment",
"emotion",
"es",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T17:19:56+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #sentiment #emotion #es #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| electricidad-small-discriminator-finetuned-clasificacion-texto-suicida
======================================================================
This model is a fine-tuned version of mrm8488/electricidad-small-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0458
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0\n* Py... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #sentiment #emotion #es #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 668419758
- CO2 Emissions (in grams): 0.5745216001459987
## Validation Metrics
- Loss: 0.5012844800949097
- Accuracy: 0.8057228915662651
- Precision: 0.7627627627627628
- Recall: 0.8355263157894737
- AUC: 0.868530701754386
- F1: 0.797... | {"language": "en", "tags": "autotrain", "datasets": ["bozelosp/autotrain-data-legit-keyword"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.5745216001459987} | world-wide/is-legit-kwd-march-27 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:bozelosp/autotrain-data-legit-keyword",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T18:44:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-bozelosp/autotrain-data-legit-keyword #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 668419758
- CO2 Emissions (in grams): 0.5745216001459987
## Validation Metrics
- Loss: 0.5012844800949097
- Accuracy: 0.8057228915662651
- Precision: 0.7627627627627628
- Recall: 0.8355263157894737
- AUC: 0.868530701754386
- F1: 0.797... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 668419758\n- CO2 Emissions (in grams): 0.5745216001459987",
"## Validation Metrics\n\n- Loss: 0.5012844800949097\n- Accuracy: 0.8057228915662651\n- Precision: 0.7627627627627628\n- Recall: 0.8355263157894737\n- AUC: 0.868530701... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-bozelosp/autotrain-data-legit-keyword #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 668419758\n- CO2 Emissio... |
automatic-speech-recognition | transformers |
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- [CORAA dataset](https://github.com/nilc-nlp/CORAA)
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz).
- [Mul... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["CORAA", "common_voice", "mls", "cetuc", "voxforge"], "metrics": ["wer"]} | alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization-sna | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"dataset:CORAA",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:voxforge",
"license:apache-2.0",
"model-index",
"endpoints_compatib... | null | 2022-03-26T18:58:07+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-CORAA #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-voxforge #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- CORAA dataset
- CETUC.
- Multilingual Librispeech (MLS).
- VoxForge.
- Common Voice 6.1.
## Repository
The repository ... | [
"# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets\r\n\r\nThis a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:\r\n\r\n- CORAA dataset\r\n- CETUC.\r\n- Multilingual Librispeech (MLS).\r\n- VoxForge.\r\n- Common Voice 6.1.",
"## Reposito... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-CORAA #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-voxforge #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2vec 2.0 trained with CORAA Po... |
text-classification | transformers |
# electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas
El presente modelo se encentra basado en una versión mejorada de [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator), y con el uso de la base de datos [hackathon-pln-es/comentari... | {"language": "es", "license": "apache-2.0", "tags": ["generated_from_trainer", "sentiment", "emotion", "suicide", "depresi\u00f3n", "suicidio", "espa\u00f1ol", "es", "spanish", "depression"], "metrics": ["accuracy"], "widget": [{"text": "La vida no merece la pena", "example_title": "Ejemplo 1"}, {"text": "Para vivir as... | hackathon-pln-es/electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"sentiment",
"emotion",
"suicide",
"depresión",
"suicidio",
"español",
"es",
"spanish",
"depression",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"regio... | null | 2022-03-26T18:58:16+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #generated_from_trainer #sentiment #emotion #suicide #depresión #suicidio #español #es #spanish #depression #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas
=============================================================================
El presente modelo se encentra basado en una versión mejorada de mrm8488/electricidad-small-discriminator, y con el uso de la base de datos hackathon-pln-es/comen... | [
"### Datos de entrenamiento\n\n\nComo se declaró anteriormente, el modelo se pre-entrenó basándose en la base de datos comentarios\\_depresivos, el cuál posee una cantidad de 192 347 filas de datos para el entrenamiento, 33 944 para las pruebas y 22630 para la validación.",
"### Hiper parámetros de entrenamiento\... | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #generated_from_trainer #sentiment #emotion #suicide #depresión #suicidio #español #es #spanish #depression #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Datos de entrenamiento\n\n\nComo se declaró ante... |
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. -->
# roberta_roberta_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
## Mo... | {"tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "roberta_roberta_summarization_xsum", "results": []}]} | Ayham/roberta_roberta_summarization_xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T19:07:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us
|
# roberta_roberta_summarization_xsum
This model is a fine-tuned version of [](URL on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameter... | [
"# roberta_roberta_summarization_xsum\n\nThis model is a fine-tuned version of [](URL on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-xsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_roberta_summarization_xsum\n\nThis model is a fine-tuned version of [](URL on the xsum dataset.",
"## Model description\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007
## Validation Metrics
- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895... | {"language": "unk", "tags": "autotrain", "datasets": ["YXHugging/autotrain-data-xlm-roberta-base-reviews"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1019.0229633198007} | YXHugging/autotrain-xlm-roberta-base-reviews-672119797 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:YXHugging/autotrain-data-xlm-roberta-base-reviews",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T21:05:03+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007
## Validation Metrics
- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 672119797\n- CO2 Emissions (in grams): 1019.0229633198007",
"## Validation Metrics\n\n- Loss: 0.9898674488067627\n- Accuracy: 0.5688083333333334\n- Macro F1: 0.5640966271895913\n- Micro F1: 0.5688083333333334\n- Weighted F... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 67211... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119798
- CO2 Emissions (in grams): 1013.8825767332373
## Validation Metrics
- Loss: 0.9646632075309753
- Accuracy: 0.5789333333333333
- Macro F1: 0.5775792001871465
- Micro F1: 0.5789333333333333
- Weighted F1: 0.5775792001871... | {"language": "unk", "tags": "autotrain", "datasets": ["YXHugging/autotrain-data-xlm-roberta-base-reviews"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1013.8825767332373} | YXHugging/autotrain-xlm-roberta-base-reviews-672119798 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:YXHugging/autotrain-data-xlm-roberta-base-reviews",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T21:07:59+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119798
- CO2 Emissions (in grams): 1013.8825767332373
## Validation Metrics
- Loss: 0.9646632075309753
- Accuracy: 0.5789333333333333
- Macro F1: 0.5775792001871465
- Micro F1: 0.5789333333333333
- Weighted F1: 0.5775792001871... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 672119798\n- CO2 Emissions (in grams): 1013.8825767332373",
"## Validation Metrics\n\n- Loss: 0.9646632075309753\n- Accuracy: 0.5789333333333333\n- Macro F1: 0.5775792001871465\n- Micro F1: 0.5789333333333333\n- Weighted F... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 67211... |
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_toy_train_data_random_noise_0.1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_random_noise_0.1", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_random_noise_0.1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-26T22:03:20+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_random\_noise\_0.1
===================================================
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: 0.9263
* Wer: 0.7213
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #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: 16\n* eval\\_b... |
text-generation | transformers |
# Rick | {"tags": ["conversational"]} | TheDaydreamer/ricky | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-26T22:07:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick | [
"# Rick"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374075536595505154/1_1j... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mkobach-naval-shaneaparrish/1648339620049/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mkobach-naval-shaneaparrish | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T00:04:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Matthew Kobach & Shane Parrish & Naval
@mkobach-naval-shaneaparrish
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, chec... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_random_noise
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_random_noise", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_random_noise | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T00:14:26+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_random\_noise
==============================================
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: 1.0909
* Wer: 0.7351
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #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: 16\n* eval\\_b... |
fill-mask | transformers | ## JavaRoBERTa-Tara
A RoBERTa model pretrained on, code_search_net Java software code.
### Training Data
The model was trained on 10,223,695 Java files retrieved from open source projects on GitHub.
### Training Objective
A MLM (Masked Language Model) objective was used to train this model.
### Usage
```python
fro... | {"language": ["java", "code"], "license": "apache-2.0", "datasets": ["code_search_net"], "widget": [{"text": "public <mask> isOdd(Integer num){if (num % 2 == 0) {return \"even\";} else {return \"odd\";}}"}]} | emre/java-RoBERTa-Tara-small | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"dataset:code_search_net",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T00:30:18+00:00 | [] | [
"java",
"code"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #dataset-code_search_net #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ## JavaRoBERTa-Tara
A RoBERTa model pretrained on, code_search_net Java software code.
### Training Data
The model was trained on 10,223,695 Java files retrieved from open source projects on GitHub.
### Training Objective
A MLM (Masked Language Model) objective was used to train this model.
### Usage
### Why Tar... | [
"## JavaRoBERTa-Tara \nA RoBERTa model pretrained on, code_search_net Java software code.",
"### Training Data\nThe model was trained on 10,223,695 Java files retrieved from open source projects on GitHub.",
"### Training Objective\nA MLM (Masked Language Model) objective was used to train this model.",
"### ... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #dataset-code_search_net #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## JavaRoBERTa-Tara \nA RoBERTa model pretrained on, code_search_net Java software code.",
"### Training Data\nThe model was trained on 10,223,... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119799
- CO2 Emissions (in grams): 1583.7188188958198
## Validation Metrics
- Loss: 0.9590993523597717
- Accuracy: 0.5827541666666667
- Macro F1: 0.5806748283026683
- Micro F1: 0.5827541666666667
- Weighted F1: 0.5806748283026... | {"language": "unk", "tags": "autotrain", "datasets": ["YXHugging/autotrain-data-xlm-roberta-base-reviews"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1583.7188188958198} | YXHugging/autotrain-xlm-roberta-base-reviews-672119799 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:YXHugging/autotrain-data-xlm-roberta-base-reviews",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T00:52:19+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119799
- CO2 Emissions (in grams): 1583.7188188958198
## Validation Metrics
- Loss: 0.9590993523597717
- Accuracy: 0.5827541666666667
- Macro F1: 0.5806748283026683
- Micro F1: 0.5827541666666667
- Weighted F1: 0.5806748283026... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 672119799\n- CO2 Emissions (in grams): 1583.7188188958198",
"## Validation Metrics\n\n- Loss: 0.9590993523597717\n- Accuracy: 0.5827541666666667\n- Macro F1: 0.5806748283026683\n- Micro F1: 0.5827541666666667\n- Weighted F... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 67211... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119800
- CO2 Emissions (in grams): 2011.6528745969179
## Validation Metrics
- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346... | {"language": "unk", "tags": "autotrain", "datasets": ["YXHugging/autotrain-data-xlm-roberta-base-reviews"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2011.6528745969179} | YXHugging/autotrain-xlm-roberta-base-reviews-672119800 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:YXHugging/autotrain-data-xlm-roberta-base-reviews",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T00:59:23+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119800
- CO2 Emissions (in grams): 2011.6528745969179
## Validation Metrics
- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 672119800\n- CO2 Emissions (in grams): 2011.6528745969179",
"## Validation Metrics\n\n- Loss: 0.9570887088775635\n- Accuracy: 0.5830708333333333\n- Macro F1: 0.5789149828346194\n- Micro F1: 0.5830708333333333\n- Weighted F... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 67211... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119801
- CO2 Emissions (in grams): 999.5670927087938
## Validation Metrics
- Loss: 0.9767692685127258
- Accuracy: 0.5738333333333333
- Macro F1: 0.5698748846905103
- Micro F1: 0.5738333333333333
- Weighted F1: 0.56987488469051... | {"language": "unk", "tags": "autotrain", "datasets": ["YXHugging/autotrain-data-xlm-roberta-base-reviews"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 999.5670927087938} | YXHugging/autotrain-xlm-roberta-base-reviews-672119801 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:YXHugging/autotrain-data-xlm-roberta-base-reviews",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T00:21:43+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119801
- CO2 Emissions (in grams): 999.5670927087938
## Validation Metrics
- Loss: 0.9767692685127258
- Accuracy: 0.5738333333333333
- Macro F1: 0.5698748846905103
- Micro F1: 0.5738333333333333
- Weighted F1: 0.56987488469051... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 672119801\n- CO2 Emissions (in grams): 999.5670927087938",
"## Validation Metrics\n\n- Loss: 0.9767692685127258\n- Accuracy: 0.5738333333333333\n- Macro F1: 0.5698748846905103\n- Micro F1: 0.5738333333333333\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 67211... |
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. -->
# bert-base-uncased-finetuned-mnli-512-10
This model is a fine-tuned version of [yy642/bert-base-uncased-finetuned-mnli-512-5](htt... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-mnli-512-10", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}, "metrics": [{"type": "... | yy642/bert-base-uncased-finetuned-mnli-512-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T00:55:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-mnli-512-10
=======================================
This model is a fine-tuned version of yy642/bert-base-uncased-finetuned-mnli-512-5 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4991
* Accuracy: 0.9356
Model description
-----------------
M... | [
"### 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #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* train\\_... |
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_toy_train_data_slow_10pct
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data_slow_10pct", "results": []}]} | scasutt/wav2vec2-base_toy_train_data_slow_10pct | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T01:28:24+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data\_slow\_10pct
============================================
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: 1.3248
* Wer: 0.7175
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1507859834107879426/d5Jq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/psimon365/1648349798068/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/psimon365 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T01:56:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Psimon
@psimon365
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | imyday/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T02:09:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2282
* Accuracy: 0.923
* F1: 0.9233
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 #tensorboard #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* learn... |
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_toy_train_data
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base_toy_train_data", "results": []}]} | scasutt/wav2vec2-base_toy_train_data | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T03:48:00+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base\_toy\_train\_data
===============================
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: 1.2522
* Wer: 0.7297
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
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"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | PaddyP/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T05:12:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2302
* Accuracy: 0.922
* F1: 0.9218
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: 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 #tensorboard #distilbert #text-classification #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\\_b... |
text2text-generation | transformers |
# ByT5-Korean - base
ByT5-Korean is a Korean specific extension of Google's [ByT5](https://github.com/google-research/byt5).
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the B... | {"license": "apache-2.0", "datasets": ["mc4"]} | everdoubling/byt5-Korean-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T05:46:11+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5-Korean - base
ByT5-Korean is a Korean specific extension of Google's ByT5.
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the ByT5's utf-8 encoding allows generic encodin... | [
"# ByT5-Korean - base\r\n\r\nByT5-Korean is a Korean specific extension of Google's ByT5.\r\n\r\nA Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.\r\nWhile the ByT5's utf-8 encoding allows ge... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5-Korean - base\r\n\r\nByT5-Korean is a Korean specific extension of Google's ByT5.\r\n\r\nA Korean syllable has three components (c... |
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_common_voice_accents_indian_only_rerun
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2_common_voice_accents_indian_only_rerun", "results": []}]} | willcai/wav2vec2_common_voice_accents_indian_only_rerun | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T05:51:10+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2\_common\_voice\_accents\_indian\_only\_rerun
=====================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2807
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 384\n* total\\_eval\\_batch\\_size: 32\n*... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\... |
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-russian
#
модель для распознания аудио. результаты модели можно потом прогнать через мою сеть исправления текстов UrukH... | {"tags": ["generated_from_trainer"], "widget": [{"src": "https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3"}], "model-index": [{"name": "wav2vec2-russian", "results": []}]} | UrukHan/wav2vec2-russian | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T06:09:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-russian
#
модель для распознания аудио. результаты модели можно потом прогнать через мою сеть исправления текстов UrukHan/t5-russian-spell
<table border="0">
<tr>
<td><b style="font-size:30px">Output wav2vec2</b></td>
<td><b style="font-size:30px">Output spell correcor</b></td>
</tr>
<tr>
... | [
"# wav2vec2-russian",
"# Запуск сети пример в колабе URL",
"# Тренировка модели с обработкой данных и созданием датасета разобрать можете в колабе:\n # URL"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec2-russian",
"# Запуск сети пример в колабе URL",
"# Тренировка модели с обработкой данных и созданием датасета разобрать можете в колабе:\... |
null | null | ## Introduction
Please see <https://github.com/k2-fsa/icefall/pull/271> for more details.
| {} | csukuangfj/icefall-asr-librispeech-stateless-transducer-2022-03-27 | null | [
"tensorboard",
"region:us"
] | null | 2022-03-27T06:29:38+00:00 | [] | [] | TAGS
#tensorboard #region-us
| ## Introduction
Please see <URL for more details.
| [
"## Introduction\n\nPlease see <URL for more details."
] | [
"TAGS\n#tensorboard #region-us \n",
"## Introduction\n\nPlease see <URL for more details."
] |
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. -->
# Example
This model is a fine-tuned version of [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3s... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Example", "results": []}]} | Danik51002/Example | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T06:58:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Example
This model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpar... | [
"# Example\n\nThis model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proced... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Example\n\nThis model is a fine-tuned version of sberbank-ai/rugpt3small_based_on_gpt2 on an unknown dataset.",
"## Model descriptio... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_data
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_data", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_data | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T07:49:37+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_data
========================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6357
* Wer: 0.5496
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_ba... |
null | keras |
# SPARQL Query Validation model
## Model description
## Intended uses & limitations
### How to use
| {"language": "en", "license": "apache-2.0", "tags": ["kgqa", "question answering", "sparql", "bert-base-cased"]} | perevalov/query-validation-lcquad | null | [
"keras",
"kgqa",
"question answering",
"sparql",
"bert-base-cased",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-03-27T08:51:36+00:00 | [] | [
"en"
] | TAGS
#keras #kgqa #question answering #sparql #bert-base-cased #en #license-apache-2.0 #region-us
|
# SPARQL Query Validation model
## Model description
## Intended uses & limitations
### How to use
| [
"# SPARQL Query Validation model",
"## Model description",
"## Intended uses & limitations",
"### How to use"
] | [
"TAGS\n#keras #kgqa #question answering #sparql #bert-base-cased #en #license-apache-2.0 #region-us \n",
"# SPARQL Query Validation model",
"## Model description",
"## Intended uses & limitations",
"### How to use"
] |
image-to-text | 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. -->
# Image-caption-generator
This model is trained on [Flickr8k](https://www.kaggle.com/datasets/nunenuh/flickr8k) dataset to generat... | {"tags": ["image-captioning", "image-to-text"], "model-index": [{"name": "image-caption-generator", "results": []}]} | bipin/image-caption-generator | null | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-captioning",
"image-to-text",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-27T08:56:24+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #image-captioning #image-to-text #endpoints_compatible #has_space #region-us
|
# Image-caption-generator
This model is trained on Flickr8k dataset to generate captions given an image.
It achieves the following results on the evaluation set:
- eval_loss: 0.2536
- eval_runtime: 25.369
- eval_samples_per_second: 63.818
- eval_steps_per_second: 8.002
- epoch: 4.0
- step: 3236
# Running the mode... | [
"# Image-caption-generator\n\nThis model is trained on Flickr8k dataset to generate captions given an image.\n\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2536\n- eval_runtime: 25.369\n- eval_samples_per_second: 63.818\n- eval_steps_per_second: 8.002\n- epoch: 4.0\n- step: 3236",
"# ... | [
"TAGS\n#transformers #pytorch #safetensors #vision-encoder-decoder #image-captioning #image-to-text #endpoints_compatible #has_space #region-us \n",
"# Image-caption-generator\n\nThis model is trained on Flickr8k dataset to generate captions given an image.\n\nIt achieves the following results on the evaluation s... |
null | keras |
# SPARQL Query Validation model
## Model description
## Intended uses & limitations
### How to use
| {"language": "en", "license": "apache-2.0", "tags": ["kgqa", "question answering", "sparql", "bert-base-cased"]} | perevalov/query-validation-rubq | null | [
"keras",
"kgqa",
"question answering",
"sparql",
"bert-base-cased",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-03-27T09:52:12+00:00 | [] | [
"en"
] | TAGS
#keras #kgqa #question answering #sparql #bert-base-cased #en #license-apache-2.0 #region-us
|
# SPARQL Query Validation model
## Model description
## Intended uses & limitations
### How to use
| [
"# SPARQL Query Validation model",
"## Model description",
"## Intended uses & limitations",
"### How to use"
] | [
"TAGS\n#keras #kgqa #question answering #sparql #bert-base-cased #en #license-apache-2.0 #region-us \n",
"# SPARQL Query Validation model",
"## Model description",
"## Intended uses & limitations",
"### How to use"
] |
text-classification | transformers | ## Frequently Asked Questions classifier
This model is trained to determine whether a question/statement is a FAQ, in the domain of products, businesses, website faqs, etc.
For e.g `"What is the warranty of your product?"` In contrast, daily questions such as `"how are you?"`, `"what is your name?"`, or simple stateme... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | timpal0l/xlm-roberta-base-faq-extractor | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"sequence-classification",
"xlm-roberta-base",
"faq",
"questions",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",... | null | 2022-03-27T10:40:45+00:00 | [] | [
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"i... | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #sequence-classification #xlm-roberta-base #faq #questions #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km ... | ## Frequently Asked Questions classifier
This model is trained to determine whether a question/statement is a FAQ, in the domain of products, businesses, website faqs, etc.
For e.g '"What is the warranty of your product?"' In contrast, daily questions such as '"how are you?"', '"what is your name?"', or simple stateme... | [
"## Frequently Asked Questions classifier\nThis model is trained to determine whether a question/statement is a FAQ, in the domain of products, businesses, website faqs, etc. \nFor e.g '\"What is the warranty of your product?\"' In contrast, daily questions such as '\"how are you?\"', '\"what is your name?\"', or s... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #sequence-classification #xlm-roberta-base #faq #questions #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #k... |
image-classification | transformers |
# pneumonia-bielefeld-dl-course
This registry contains the model for making pneumonia predictions and was prepared for
Bielefeld University Deep Learning course homework.
The code used for this implementation mostly comes from here: https://github.com/nateraw/huggingpics it was a ready pipeline for model fine-tuni... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | eren23/pneumonia-bielefeld-dl-course | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T11:17:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pneumonia-bielefeld-dl-course
This registry contains the model for making pneumonia predictions and was prepared for
Bielefeld University Deep Learning course homework.
The code used for this implementation mostly comes from here: URL it was a ready pipeline for model fine-tuning with huggingface and PyTorch Lig... | [
"# pneumonia-bielefeld-dl-course\n\n\nThis registry contains the model for making pneumonia predictions and was prepared for \nBielefeld University Deep Learning course homework.\n\nThe code used for this implementation mostly comes from here: URL it was a ready pipeline for model fine-tuning with huggingface and P... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pneumonia-bielefeld-dl-course\n\n\nThis registry contains the model for making pneumonia predictions and was prepared for \nBielefeld University Deep Learn... |
text2text-generation | transformers |
# Chinese BART
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper]... | {"language": "zh", "datasets": "CLUECorpusSmall", "widget": [{"text": "\u4f5c\u4e3a\u7535\u5b50[MASK]\u7684\u5e73\u53f0\uff0c\u4eac\u4e1c\u7edd\u5bf9\u662f\u9886\u5148\u8005\u3002\u5982\u4eca\u7684\u5218\u5f3a[MASK]\u5df2\u7ecf\u662f\u8eab\u4ef7\u8fc7[MASK]\u7684\u8001\u677f\u3002"}]} | uer/bart-large-chinese-cluecorpussmall | null | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:2212.06385",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T11:24:39+00:00 | [
"1909.05658",
"2212.06385"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #bart #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us
| Chinese BART
============
Model description
-----------------
This model is pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it t... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #bart #text2text-generation #zh #dataset-CLUECorpusSmall #arxiv-1909.05658 #arxiv-2212.06385 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
question-answering | transformers |
## MODEL DESCRIPTION
huBERT base model (cased) fine-tuned on SQuAD v1
- huBert model + Tokenizer: https://huggingface.co/SZTAKI-HLT/hubert-base-cc
- Hungarian SQUAD v1 dataset: Machine Translated SQuAD dataset (Google Translate API)
- This is a demo model. Date of publication: 2022.03.27.
## Model in action
- Fast ... | {"language": "hu", "tags": ["question-answering", "bert"], "widget": [{"text": "Melyik foly\u00f3 szeli kett\u00e9 Budapestet?", "context": "Magyarorsz\u00e1g f\u0151v\u00e1ros\u00e1t, Budapestet a Duna foly\u00f3 szeli kett\u00e9. A XIX. sz\u00e1zadban \u00e9p\u00fclt L\u00e1nch\u00edd a dimbes-dombos budai oldalt k\u... | mcsabai/huBert-fine-tuned-hungarian-squadv1 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"hu",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T11:35:44+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us
|
## MODEL DESCRIPTION
huBERT base model (cased) fine-tuned on SQuAD v1
- huBert model + Tokenizer: URL
- Hungarian SQUAD v1 dataset: Machine Translated SQuAD dataset (Google Translate API)
- This is a demo model. Date of publication: 2022.03.27.
## Model in action
- Fast usage with pipelines:
| [
"## MODEL DESCRIPTION\n\nhuBERT base model (cased) fine-tuned on SQuAD v1\n- huBert model + Tokenizer: URL\n- Hungarian SQUAD v1 dataset: Machine Translated SQuAD dataset (Google Translate API)\n- This is a demo model. Date of publication: 2022.03.27.",
"## Model in action\n\n- Fast usage with pipelines:"
] | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us \n",
"## MODEL DESCRIPTION\n\nhuBERT base model (cased) fine-tuned on SQuAD v1\n- huBert model + Tokenizer: URL\n- Hungarian SQUAD v1 dataset: Machine Translated SQuAD dataset (Google Translate API)\n- This is a demo ... |
audio-classification | transformers |
# wav2vec2-base-finetuned-sentiment-mesd
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [MESD](https://huggingface.co/hackathon-pln-es/MESD) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5729
- Accuracy: 0.8308
## Mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-sentiment-mesd", "results": []}]} | hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-27T12:23:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec2-base-finetuned-sentiment-mesd
======================================
This model is a fine-tuned version of facebook/wav2vec2-base on the MESD dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5729
* Accuracy: 0.8308
Model description
-----------------
This model was trained to... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.25e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and eps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
null | null | ## Introduction
Please see <https://github.com/k2-fsa/icefall/pull/271> for more details.
| {} | csukuangfj/icefall-asr-librispeech-stateless-transducer-2022-03-27-2 | null | [
"tensorboard",
"region:us"
] | null | 2022-03-27T12:27:21+00:00 | [] | [] | TAGS
#tensorboard #region-us
| ## Introduction
Please see <URL for more details.
| [
"## Introduction\n\nPlease see <URL for more details."
] | [
"TAGS\n#tensorboard #region-us \n",
"## Introduction\n\nPlease see <URL for more details."
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-hindicone
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-hindicone", "results": []}]} | SAGAR4REAL/wav2vec2-large-hindicone | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T12:41:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-hindicone
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# wav2vec2-large-hindicone\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-hindicone\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset."... |
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. -->
# t5small4-squad1024
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5small4-squad1024", "results": []}]} | Splend1dchan/t5small4-squad1024 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T13:15:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5small4-squad1024
This model is a fine-tuned version of t5-small on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The follo... | [
"# t5small4-squad1024\n\nThis model is a fine-tuned version of t5-small on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5small4-squad1024\n\nThis model is a fine-tuned version of t5-small on an unknown dataset.",
"## Model descr... |
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. -->
# gpt2-small-spanish-disco-poetry
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/dat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-small-spanish-disco-poetry", "results": []}]} | jorge-henao/gpt2-small-spanish-disco-poetry | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T13:48:15+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
| gpt2-small-spanish-disco-poetry
===============================
This model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2471
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\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... |
null | transformers |
# ColBERTer (Dim: 32) for Passage Retrieval
If you want to know more about our ColBERTer architecture check out our paper: https://arxiv.org/abs/2203.13088 🎉
For more information, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/colberter
## Limitations & Bias
... | {"language": "en", "license": "apache-2.0", "tags": ["bag-of-words", "dense-passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/colberter-128-32-msmarco | null | [
"transformers",
"pytorch",
"ColBERT",
"bag-of-words",
"dense-passage-retrieval",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2203.13088",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T13:59:48+00:00 | [
"2203.13088"
] | [
"en"
] | TAGS
#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us
|
# ColBERTer (Dim: 32) for Passage Retrieval
If you want to know more about our ColBERTer architecture check out our paper: URL
For more information, source code, and a minimal usage example please visit: URL
## Limitations & Bias
- The model is only trained on english text.
- The model inherits social... | [
"# ColBERTer (Dim: 32) for Passage Retrieval\r\n\r\nIf you want to know more about our ColBERTer architecture check out our paper: URL \r\n\r\nFor more information, source code, and a minimal usage example please visit: URL",
"## Limitations & Bias\r\n\r\n- The model is only trained on english text.\r\n\r\n- The ... | [
"TAGS\n#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# ColBERTer (Dim: 32) for Passage Retrieval\r\n\r\nIf you want to know more about our ColBERTer architecture chec... |
null | transformers |
# Uni-ColBERTer (Dim: 1) for Passage Retrieval
If you want to know more about our (Uni-)ColBERTer architecture check out our paper: https://arxiv.org/abs/2203.13088 🎉
For more information, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/colberter
## Limitation... | {"language": "en", "license": "apache-2.0", "tags": ["bag-of-words", "dense-passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/uni-colberter-128-1-msmarco | null | [
"transformers",
"pytorch",
"ColBERT",
"bag-of-words",
"dense-passage-retrieval",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2203.13088",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T14:09:28+00:00 | [
"2203.13088"
] | [
"en"
] | TAGS
#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us
|
# Uni-ColBERTer (Dim: 1) for Passage Retrieval
If you want to know more about our (Uni-)ColBERTer architecture check out our paper: URL
For more information, source code, and a minimal usage example please visit: URL
## Limitations & Bias
- The model is only trained on english text.
- The model inheri... | [
"# Uni-ColBERTer (Dim: 1) for Passage Retrieval\r\n\r\nIf you want to know more about our (Uni-)ColBERTer architecture check out our paper: URL \r\n\r\nFor more information, source code, and a minimal usage example please visit: URL",
"## Limitations & Bias\r\n\r\n- The model is only trained on english text.\r\n\... | [
"TAGS\n#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uni-ColBERTer (Dim: 1) for Passage Retrieval\r\n\r\nIf you want to know more about our (Uni-)ColBERTer architec... |
sentence-similarity | sentence-transformers |
# sentence-IT5-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 ([IT5](https://huggingface.co/gsarti/it5-small)) small model trained for asymmetric seman... | {"language": ["it"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | efederici/sentence-it5-small | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"transformers",
"it",
"endpoints_compatible",
"region:us"
] | null | 2022-03-27T14:19:10+00:00 | [] | [
"it"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #it #endpoints_compatible #region-us
|
# sentence-IT5-small
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 (IT5) small model trained for asymmetric semantic search. Query is a keyword, Paragraph is a short news article.
... | [
"# sentence-IT5-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is a T5 (IT5) small model trained for asymmetric semantic search. Query is a keyword, Paragraph is a short news arti... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #it #endpoints_compatible #region-us \n",
"# sentence-IT5-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clusteri... |
text-generation | transformers |
# Lavenza DialoGPT Model | {"tags": ["conversational"]} | BeamBee/DialoGPT-small-Lavenza | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-27T15:13:24+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Lavenza DialoGPT Model | [
"# Lavenza DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Lavenza DialoGPT Model"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.