pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: ... | {} | BigSalmon/GPT2Neo1.3BPoints3 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T18:15:14+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | # BART-base fine-tuned on NaturalQuestions for **Question Generation**
[BART Model](https://arxiv.org/pdf/1910.13461.pdf) trained for Question Generation in an unsupervised manner using [Self-Training](https://arxiv.org/pdf/2104.08801.pdf) algorithm (Kulshreshtha et al, EMNLP 2021). The dataset used are unaligned ques... | {"license": "cc-by-4.0"} | McGill-NLP/bart-qg-mlquestions-selftraining | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.13461",
"arxiv:2104.08801",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T18:15:21+00:00 | [
"1910.13461",
"2104.08801"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-1910.13461 #arxiv-2104.08801 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # BART-base fine-tuned on NaturalQuestions for Question Generation
BART Model trained for Question Generation in an unsupervised manner using Self-Training algorithm (Kulshreshtha et al, EMNLP 2021). The dataset used are unaligned questions and passages from MLQuestions dataset.
## Details of Self-Training
The Self-... | [
"# BART-base fine-tuned on NaturalQuestions for Question Generation\n\nBART Model trained for Question Generation in an unsupervised manner using Self-Training algorithm (Kulshreshtha et al, EMNLP 2021). The dataset used are unaligned questions and passages from MLQuestions dataset.",
"## Details of Self-Training... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-1910.13461 #arxiv-2104.08801 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART-base fine-tuned on NaturalQuestions for Question Generation\n\nBART Model trained for Question Generation in an unsupervised manner u... |
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. -->
# sagemaker-distilbert-emotion-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion-1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "defau... | lewtun/sagemaker-distilbert-emotion-1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T18:17:10+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-emotion-1
==============================
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: 0.1651
* Accuracy: 0.9325
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3... |
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. -->
# 20220412-203254
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "20220412-203254", "results": []}]} | lilitket/20220412-203254 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T19:33:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| 20220412-203254
===============
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: 5.0428
* Wer: 1.0019
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 1\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 #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: 6e-06\n* train\\_batch\\... |
fill-mask | transformers |
# afro-xlmr-base
AfroXLMR-base was created by MLM adaptation of XLM-R-base model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high-re... | {"language": ["en", "fr", "ar", "ha", "ig", "yo", "rn", "rw", "sn", "xh", "zu", "om", "am", "so", "st", "ny", "mg", "sw", "af"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "afro-xlmr-base", "results": []}]} | Davlan/afro-xlmr-base | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"en",
"fr",
"ar",
"ha",
"ig",
"yo",
"rn",
"rw",
"sn",
"xh",
"zu",
"om",
"am",
"so",
"st",
"ny",
"mg",
"sw",
"af",
"doi:10.57967/hf/0005",
"license:mit",
"autotrain_co... | null | 2022-04-12T19:47:53+00:00 | [] | [
"en",
"fr",
"ar",
"ha",
"ig",
"yo",
"rn",
"rw",
"sn",
"xh",
"zu",
"om",
"am",
"so",
"st",
"ny",
"mg",
"sw",
"af"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #generated_from_trainer #en #fr #ar #ha #ig #yo #rn #rw #sn #xh #zu #om #am #so #st #ny #mg #sw #af #doi-10.57967/hf/0005 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| afro-xlmr-base
==============
AfroXLMR-base was created by MLM adaptation of XLM-R-base model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families a... | [
"### BibTeX entry and citation info."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #generated_from_trainer #en #fr #ar #ha #ig #yo #rn #rw #sn #xh #zu #om #am #so #st #ny #mg #sw #af #doi-10.57967/hf/0005 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info."
] |
fill-mask | transformers |
# afro-xlmr-small
AfroXLMR-small was created by [first reducing the vocabulary token size](https://aclanthology.org/2020.sustainlp-1.16/) of XLM-R-base from 250K to 70k, followed by MLM adaptation on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona,... | {"license": "mit"} | Davlan/afro-xlmr-small | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T19:48:17+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| afro-xlmr-small
===============
AfroXLMR-small was created by first reducing the vocabulary token size of XLM-R-base from 250K to 70k, followed by MLM adaptation on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa,... | [
"### BibTeX entry and citation info."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info."
] |
text-generation | transformers |
# Philip DialoGPT Model | {"tags": ["conversational"]} | NonzeroCornet34/DialoGPT-small-philbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-12T20:15:22+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Philip DialoGPT Model | [
"# Philip DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Philip DialoGPT Model"
] |
sentence-similarity | sentence-transformers |
# clip-ViT-L-14
This is the Image & Text model [CLIP](https://arxiv.org/abs/2103.00020), which maps text and images to a shared vector space. For applications of the models, have a look in our documentation [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html)
## Usage
Aft... | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/clip-ViT-L-14 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:2103.00020",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T20:28:48+00:00 | [
"2103.00020"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2103.00020 #endpoints_compatible #has_space #region-us
| clip-ViT-L-14
=============
This is the Image & Text model CLIP, which maps text and images to a shared vector space. For applications of the models, have a look in our documentation URL - Image Search
Usage
-----
After installing sentence-transformers ('pip install sentence-transformers'), the usage of this mode... | [] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2103.00020 #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# clip-ViT-B-16
This is the Image & Text model [CLIP](https://arxiv.org/abs/2103.00020), which maps text and images to a shared vector space. For applications of the models, have a look in our documentation [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html)
## Usage
Aft... | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/clip-ViT-B-16 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:2103.00020",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T20:32:14+00:00 | [
"2103.00020"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2103.00020 #endpoints_compatible #has_space #region-us
| clip-ViT-B-16
=============
This is the Image & Text model CLIP, which maps text and images to a shared vector space. For applications of the models, have a look in our documentation URL - Image Search
Usage
-----
After installing sentence-transformers ('pip install sentence-transformers'), the usage of this mode... | [] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2103.00020 #endpoints_compatible #has_space #region-us \n"
] |
null | null | These are files for the trained protein localization prediction model PB-Chlamy, created for the paper **"A Chloroplast Protein Atlas Reveals Novel Structures and Spatial Organization of Biosynthetic Pathways"** by Lianyong Wang, Weronika Patena, Kelly A. Van Baalen, Yihua Xie, Emily R. Singer, Sophia Gavrilenko, Miche... | {} | wpatena/PB-Chlamy | null | [
"region:us"
] | null | 2022-04-12T21:35:19+00:00 | [] | [] | TAGS
#region-us
| These are files for the trained protein localization prediction model PB-Chlamy, created for the paper "A Chloroplast Protein Atlas Reveals Novel Structures and Spatial Organization of Biosynthetic Pathways" by Lianyong Wang, Weronika Patena, Kelly A. Van Baalen, Yihua Xie, Emily R. Singer, Sophia Gavrilenko, Michelle ... | [] | [
"TAGS\n#region-us \n"
] |
null | null | hehexd | {} | huggan/projected_gan_upload_test | null | [
"region:us"
] | null | 2022-04-12T23:10:47+00:00 | [] | [] | TAGS
#region-us
| hehexd | [] | [
"TAGS\n#region-us \n"
] |
feature-extraction | transformers | # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
[](https://github.com/voidism/DiffCSE/)
[](https://colab.research.google.com/github/voidi... | {"license": "apache-2.0"} | voidism/diffcse-bert-base-uncased-sts | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2204.10298",
"arxiv:2104.08821",
"arxiv:2111.00899",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T23:27:46+00:00 | [
"2204.10298",
"2104.08821",
"2111.00899"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #region-us
| # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
![GitHub Stars](URL
![Open In Colab](URL
arXiv link: URL
To be published in NAACL 2022
Authors:
Yung-Sung Chuang,
Rumen Dangovski,
Hongyin Luo,
Yang Zhang,
Shiyu Chang,
Marin Soljačić,
Shang-Wen Li,
Scott Wen-tau Yih,
Yoon Kim,
James Glass... | [
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n![GitHub Stars](URL\n\n![Open In Colab](URL\n\narXiv link: URL \nTo be published in NAACL 2022\n\nAuthors:\nYung-Sung Chuang, \nRumen Dangovski,\nHongyin Luo,\nYang Zhang,\nShiyu Chang,\nMarin Soljačić,\nShang-Wen Li,\nScott Wen-tau Yih,... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2204.10298 #arxiv-2104.08821 #arxiv-2111.00899 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings \n\n
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | {"license": "bsd-3-clause"} | Salesforce/codegen-6B-mono | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T23:51:11+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Mono 6B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origin... | [
"# CodeGen (CodeGen-Mono 6B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-Mono 6B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pape... |
text-generation | transformers | # CodeGen (CodeGen-Multi 6B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savar... | {"license": "bsd-3-clause"} | Salesforce/codegen-6B-multi | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T23:51:28+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Multi 6B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origi... | [
"# CodeGen (CodeGen-Multi 6B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The model... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-Multi 6B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pap... |
text-generation | transformers | # CodeGen (CodeGen-NL 6B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese... | {"license": "bsd-3-clause"} | Salesforce/codegen-6B-nl | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T23:51:41+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-NL 6B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are original... | [
"# CodeGen (CodeGen-NL 6B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-NL 6B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper:... |
text-generation | transformers | # CodeGen (CodeGen-NL 16B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savares... | {"license": "bsd-3-clause"} | Salesforce/codegen-16B-nl | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T23:51:56+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-NL 16B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origina... | [
"# CodeGen (CodeGen-NL 16B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models ... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-NL 16B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper... |
text-generation | transformers | # CodeGen (CodeGen-Multi 16B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Sava... | {"license": "bsd-3-clause"} | Salesforce/codegen-16B-multi | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T23:52:09+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Multi 16B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are orig... | [
"# CodeGen (CodeGen-Multi 16B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The mode... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-Multi 16B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pa... |
text-generation | transformers | # CodeGen (CodeGen-Mono 16B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savar... | {"license": "bsd-3-clause"} | Salesforce/codegen-16B-mono | null | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-12T23:52:21+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CodeGen (CodeGen-Mono 16B)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are origi... | [
"# CodeGen (CodeGen-Mono 16B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The model... | [
"TAGS\n#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CodeGen (CodeGen-Mono 16B)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the pap... |
unconditional-image-generation | null |
# Generate aurora image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-aurora"]} | huggan/fastgan-few-shot-aurora | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-aurora",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-12T23:52:51+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-aurora #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate aurora image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the m... | [
"# Generate aurora image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-enco... | [
"TAGS\n#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-aurora #arxiv-2101.04775 #license-mit #has_space #region-us \n",
"# Generate aurora image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fid... |
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-devices-sum-ver3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-devices-sum-ver3", "results": []}]} | Wizounovziki/t5-small-devices-sum-ver3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T01:44:23+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
| t5-small-devices-sum-ver3
=========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1325
* Rouge1: 95.6631
* Rouge2: 83.6149
* Rougel: 95.6622
* Rougelsum: 95.6632
* Gen Len: 4.9279
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"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",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-mirst500-ac
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["mir_st500"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-mirst500-ac", "results": []}]} | gary109/wav2vec2-base-mirst500-ac | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:mir_st500",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T02:18:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-mirst500-ac
=========================
This model is a fine-tuned version of facebook/wav2vec2-base on the /workspace/datasets/datasets/MIR\_ST500/MIR\_ST500.py dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7566
* Accuracy: 0.7570
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 1\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch... |
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. -->
# codeparrot-ds-sample-1ep-12apr
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-sample-1ep-12apr", "results": []}]} | mimicheng/codeparrot-ds-sample-1ep-12apr | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T02:45:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| codeparrot-ds-sample-1ep-12apr
==============================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9947
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* distributed\\_type: tpu\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #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: 0.0005\n... |
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. -->
# bart-large-cnn-finetuned-multi-news
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/faceb... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-multi-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "mul... | nikhedward/bart-large-cnn-finetuned-multi-news | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T03:36:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-multi_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bart-large-cnn-finetuned-multi-news
===================================
This model is a fine-tuned version of facebook/bart-large-cnn on the multi\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0950
* Rouge1: 42.0423
* Rouge2: 14.8812
* Rougel: 23.3412
* Rougelsum: 36.2613
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-multi_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
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/1428572680882688005/rqGx... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/radfemman/1649830938917/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/radfemman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T04:44:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Radfem Ally 🇺🇸
@radfemman
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... | amir36/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 | 2022-04-13T04:57:20+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: 0.2180
* Accuracy: 0.921
* F1: 0.9210
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... |
text-classification | transformers | # INT8 albert-base-v2-sst2
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "neural-compressor", "PostTrainingStatic"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classificat... | Intel/albert-base-v2-sst2-int8-static | null | [
"transformers",
"pytorch",
"onnx",
"albert",
"text-classification",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"neural-compressor",
"PostTrainingStatic",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T06:26:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #albert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| INT8 albert-base-v2-sst2
========================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Alireza1044/albert-base-v2-sst2.
The calibration data... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model Alireza1044/albert-base-v2-sst2.\n\n\nThe calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch s... | [
"TAGS\n#transformers #pytorch #onnx #albert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized wi... |
sentence-similarity | sentence-transformers | <img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>
# SHerbert large - Polish SentenceBERT
SentenceBERT is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically... | {"language": ["pl"], "license": "cc-by-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["Wikipedia"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "Uczenie maszynowe jest konsekwencj\u0105 rozwoju idei sztucznej inteligencji i metod jej wdra\u01... | Voicelab/sbert-large-cased-pl | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"pl",
"dataset:Wikipedia",
"arxiv:1908.10084",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T06:33:36+00:00 | [
"1908.10084"
] | [
"pl"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #pl #dataset-Wikipedia #arxiv-1908.10084 #license-cc-by-4.0 #endpoints_compatible #region-us
| <img src="https://public.3.URL alt="logo voicelab nlp" style="width:300px;"/>
SHerbert large - Polish SentenceBERT
====================================
SentenceBERT is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings ... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #pl #dataset-Wikipedia #arxiv-1908.10084 #license-cc-by-4.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
**NOTE: This is the FP32 version of [Facebook's official bart-large](https://huggingface.co/facebook/bart-large/edit/main/README.md).**
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generat... | {"language": "en", "license": "apache-2.0"} | patrickvonplaten/bart-large-fp32 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T06:53:21+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
NOTE: This is the FP32 version of Facebook's official bart-large.
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released ... | [
"# BART (large-sized model)\n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. \n\nDisclaimer: The team releasing BART d... | [
"TAGS\n#transformers #pytorch #jax #bart #feature-extraction #en #arxiv-1910.13461 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# BART (large-sized model)\n\nBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Na... |
null | null |
# Pix2Pix trained on the maps dataset
## Model description
This model is a [Pix2Pix](https://arxiv.org/abs/1611.07004) model trained on the [huggan/maps](https://huggingface.co/datasets/huggan/maps) dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way a... | {"license": "mit", "tags": ["huggan", "gan"], "datasets": ["huggan/maps"]} | huggan/pix2pix-maps | null | [
"pytorch",
"huggan",
"gan",
"dataset:huggan/maps",
"arxiv:1611.07004",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-13T07:11:16+00:00 | [
"1611.07004"
] | [] | TAGS
#pytorch #huggan #gan #dataset-huggan/maps #arxiv-1611.07004 #license-mit #has_space #region-us
|
# Pix2Pix trained on the maps dataset
## Model description
This model is a Pix2Pix model trained on the huggan/maps dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way around.
The model was trained using the example script provided by HuggingFace as p... | [
"# Pix2Pix trained on the maps dataset",
"## Model description\n\nThis model is a Pix2Pix model trained on the huggan/maps dataset. The goal for the model is to turn a satellite map into a geographic map à la Google Maps, and the other way around.\n\nThe model was trained using the example script provided by Hugg... | [
"TAGS\n#pytorch #huggan #gan #dataset-huggan/maps #arxiv-1611.07004 #license-mit #has_space #region-us \n",
"# Pix2Pix trained on the maps dataset",
"## Model description\n\nThis model is a Pix2Pix model trained on the huggan/maps dataset. The goal for the model is to turn a satellite map into a geographic map ... |
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. -->
# paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT-10_epochs
This model is a fine-tuned version of [sentence-transformers/parap... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT-10_epochs", "results": []}]} | veddm/paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT-10_epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T07:22:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| paraphrase-multilingual-MiniLM-L12-v2-finetuned-DIT-10\_epochs
==============================================================
This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.69... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #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\\_siz... |
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. -->
# roberta-large-finetuned-clinc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"},... | lewtun/roberta-large-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T07:40:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc
=============================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1545
* Accuracy: 0.9768
Model description
-----------------
More information needed
Intended uses & limi... | [
"### 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* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346
## Validation Metrics
- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2... | {"language": "ja", "tags": "autotrain", "datasets": ["vabadeh213/autotrain-data-iine_classification10"], "widget": [{"text": "Rust\u3067WebAssembly\u30a4\u30f3\u30bf\u30d7\u30ea\u30bf\u3092\u4f5c\u3063\u305f\u8a71+webassembly+rust"}, {"text": "Go\u306e\u30ed\u30ae\u30f3\u30b0\u30e9\u30a4\u30d6\u30e9\u30ea 2021\u5e74\u5... | vabadeh213/autotrain-iine_classification10-737422470 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"ja",
"dataset:vabadeh213/autotrain-data-iine_classification10",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T07:45:21+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-vabadeh213/autotrain-data-iine_classification10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 737422470
- CO2 Emissions (in grams): 7.351885824089346
## Validation Metrics
- Loss: 0.39456263184547424
- Accuracy: 0.8279088689991864
- Precision: 0.6869806094182825
- Recall: 0.17663817663817663
- AUC: 0.7937892215111646
- F1: 0.2... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 737422470\n- CO2 Emissions (in grams): 7.351885824089346",
"## Validation Metrics\n\n- Loss: 0.39456263184547424\n- Accuracy: 0.8279088689991864\n- Precision: 0.6869806094182825\n- Recall: 0.17663817663817663\n- AUC: 0.79378922... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-vabadeh213/autotrain-data-iine_classification10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 737422470\n- CO2 Emi... |
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. -->
# kobert-finetuned-klue-v2
This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "kobert-finetuned-klue-v2", "results": []}]} | obokkkk/kobert-finetuned-klue-v2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T07:46:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| kobert-finetuned-klue-v2
========================
This model is a fine-tuned version of monologg/kobert on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2678
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* opti... |
fill-mask | transformers |
# afro-xlmr-mini
AfroXLMR-mini was created by MLM adaptation of [XLM-R-miniLM](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhos... | {"license": "mit"} | Davlan/afro-xlmr-mini | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T08:34:21+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| afro-xlmr-mini
==============
AfroXLMR-mini was created by MLM adaptation of XLM-R-miniLM model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families... | [
"### BibTeX entry and citation info."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info."
] |
text-classification | transformers |
# Hatemoji Model
## Model description
This model is a fine-tuned version of the [DeBERTa base model](https://huggingface.co/microsoft/deberta-base). This model is cased. The model was trained on iterative rounds of adversarial data generation with human-and-model-in-the-loop. In each round, annotators are tasked wit... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["text-classification", "pytorch", "hate-speech-detection"], "datasets": ["HatemojiBuild", "HatemojiCheck"], "metrics": ["Accuracy, F1 Score"]} | HannahRoseKirk/Hatemoji | null | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"hate-speech-detection",
"en",
"dataset:HatemojiBuild",
"dataset:HatemojiCheck",
"arxiv:2108.05921",
"arxiv:2012.15761",
"arxiv:2202.11176",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T08:36:34+00:00 | [
"2108.05921",
"2012.15761",
"2202.11176"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta #text-classification #hate-speech-detection #en #dataset-HatemojiBuild #dataset-HatemojiCheck #arxiv-2108.05921 #arxiv-2012.15761 #arxiv-2202.11176 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Hatemoji Model
==============
Model description
-----------------
This model is a fine-tuned version of the DeBERTa base model. This model is cased. The model was trained on iterative rounds of adversarial data generation with human-and-model-in-the-loop. In each round, annotators are tasked with tricking the model... | [
"### Training data\n\n\nThe model was trained on:\n\n\n* The three rounds of emoji-containing, adversarially-generated texts from HatemojiBuild\n* The four rounds of text-only, adversarially-generated texts from Vidgen et al., (2021). *Learning from the worst: Dynamically generated datasets to improve online hate d... | [
"TAGS\n#transformers #pytorch #deberta #text-classification #hate-speech-detection #en #dataset-HatemojiBuild #dataset-HatemojiCheck #arxiv-2108.05921 #arxiv-2012.15761 #arxiv-2202.11176 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training data\n\n\nThe model was trained on... |
unconditional-image-generation | transformers |
# Hugging NFT: cyberkongz
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/cyberkongz).
D... | {"license": "mit", "tags": ["huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation"], "datasets": ["huggingnft/cyberkongz"]} | huggingnft/cyberkongz | null | [
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/cyberkongz",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T08:42:37+00:00 | [] | [] | TAGS
#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cyberkongz #license-mit #endpoints_compatible #region-us
|
# Hugging NFT: cyberkongz
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available here.
Dataset is available here.
Check Space: link... | [
"# Hugging NFT: cyberkongz",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright\nholder.",
"## Model description\n\nLightWeight GAN model for unconditional generation.\n\nNFT collection available here.\n\nDataset is available her... | [
"TAGS\n#transformers #huggingnft #nft #huggan #gan #image #images #unconditional-image-generation #dataset-huggingnft/cyberkongz #license-mit #endpoints_compatible #region-us \n",
"# Hugging NFT: cyberkongz",
"## Disclaimer\n\nAll rights belong to their owners. Models and datasets can be removed from the site a... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 737822494
- CO2 Emissions (in grams): 361.800665798794
## Validation Metrics
- Loss: 2.326287031173706
- Rouge1: 5.2053
- Rouge2: 1.8535
- RougeL: 5.2419
- RougeLsum: 5.228
- Gen Len: 18.3677
## Usage
You can use cURL to access this model:
... | {"language": "ja", "tags": "autotrain", "datasets": ["vabadeh213/autotrain-data-wikihow"], "widget": [{"text": "\u8105\u5a01\u3092\u611f\u3058\u305f\u86c7\u306f\u518d\u3073\u8972\u3044\u304b\u304b\u308a\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u565b\u307e\u308c\u305f\u969b\u306f\u901f\u3084\u304b\u306b\u86... | vabadeh213/autotrain-wikihow-737822494 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"ja",
"dataset:vabadeh213/autotrain-data-wikihow",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T08:47:10+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #ja #dataset-vabadeh213/autotrain-data-wikihow #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 737822494
- CO2 Emissions (in grams): 361.800665798794
## Validation Metrics
- Loss: 2.326287031173706
- Rouge1: 5.2053
- Rouge2: 1.8535
- RougeL: 5.2419
- RougeLsum: 5.228
- Gen Len: 18.3677
## Usage
You can use cURL to access this model:
... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 737822494\n- CO2 Emissions (in grams): 361.800665798794",
"## Validation Metrics\n\n- Loss: 2.326287031173706\n- Rouge1: 5.2053\n- Rouge2: 1.8535\n- RougeL: 5.2419\n- RougeLsum: 5.228\n- Gen Len: 18.3677",
"## Usage\n\nYou can use cU... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #ja #dataset-vabadeh213/autotrain-data-wikihow #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: 737822494\n- CO... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | thamaine/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T09:06:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1580
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #tf #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch... |
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | cosmo/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T09:18:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
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. -->
# javilonso/classificationEsp1_Augmented_Attraction
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationEsp1_Augmented_Attraction", "results": []}]} | javilonso/classificationEsp1_Augmented_Attraction | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T09:32:42+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/classificationEsp1\_Augmented\_Attraction
===================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0078
* Validation Loss: 0.0581
* Epoch: 2
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11565, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #roberta #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': 'AdamWeightDecay', 'learning\... |
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. -->
# focus_sum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multil... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "focus_sum", "results": []}]} | eagles/focus_sum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T09:33:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| focus\_sum
==========
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0575
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_... |
null | null | # tamil bert tokenizer 200000
## trained using oscar dataset
# [colab file link](https://colab.research.google.com/drive/1Fh07QjTILxqc-0WECd_DKVGS-s-nUH1O?usp=sharing)
| {"license": "osl-3.0"} | AswiN037/tamil-bert-tokenizer-200000 | null | [
"license:osl-3.0",
"region:us"
] | null | 2022-04-13T09:38:40+00:00 | [] | [] | TAGS
#license-osl-3.0 #region-us
| # tamil bert tokenizer 200000
## trained using oscar dataset
# colab file link
| [
"# tamil bert tokenizer 200000",
"## trained using oscar dataset",
"# colab file link"
] | [
"TAGS\n#license-osl-3.0 #region-us \n",
"# tamil bert tokenizer 200000",
"## trained using oscar dataset",
"# colab file link"
] |
fill-mask | transformers |
## mLUKE
**mLUKE** (multilingual LUKE) is a multilingual extension of LUKE.
Please check the [official repository](https://github.com/studio-ousia/luke) for
more details and updates.
This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number
of parameters in this model is 279M.
The model ... | {"language": ["multilingual", "ar", "bn", "de", "el", "en", "es", "fi", "fr", "hi", "id", "it", "ja", "ko", "nl", "pl", "pt", "ru", "sv", "sw", "te", "th", "tr", "vi", "zh"], "license": "apache-2.0", "tags": ["luke", "named entity recognition", "relation classification", "question answering"], "thumbnail": "https://git... | studio-ousia/mluke-base-lite | null | [
"transformers",
"pytorch",
"luke",
"fill-mask",
"named entity recognition",
"relation classification",
"question answering",
"multilingual",
"ar",
"bn",
"de",
"el",
"en",
"es",
"fi",
"fr",
"hi",
"id",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"sv",
"sw",
"te",
... | null | 2022-04-13T09:42:00+00:00 | [
"2010.01057"
] | [
"multilingual",
"ar",
"bn",
"de",
"el",
"en",
"es",
"fi",
"fr",
"hi",
"id",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"sv",
"sw",
"te",
"th",
"tr",
"vi",
"zh"
] | TAGS
#transformers #pytorch #luke #fill-mask #named entity recognition #relation classification #question answering #multilingual #ar #bn #de #el #en #es #fi #fr #hi #id #it #ja #ko #nl #pl #pt #ru #sv #sw #te #th #tr #vi #zh #arxiv-2010.01057 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## mLUKE
mLUKE (multilingual LUKE) is a multilingual extension of LUKE.
Please check the official repository for
more details and updates.
This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number
of parameters in this model is 279M.
The model was initialized with the weights of XLM-RoBE... | [
"## mLUKE\n\nmLUKE (multilingual LUKE) is a multilingual extension of LUKE.\n\nPlease check the official repository for\nmore details and updates.\n\nThis is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number\nof parameters in this model is 279M.\nThe model was initialized with the weight... | [
"TAGS\n#transformers #pytorch #luke #fill-mask #named entity recognition #relation classification #question answering #multilingual #ar #bn #de #el #en #es #fi #fr #hi #id #it #ja #ko #nl #pl #pt #ru #sv #sw #te #th #tr #vi #zh #arxiv-2010.01057 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #regio... |
fill-mask | transformers |
## mLUKE
**mLUKE** (multilingual LUKE) is a multilingual extension of LUKE.
Please check the [official repository](https://github.com/studio-ousia/luke) for
more details and updates.
This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number
of parameters in this model is 561M.
The model ... | {"language": ["multilingual", "ar", "bn", "de", "el", "en", "es", "fi", "fr", "hi", "id", "it", "ja", "ko", "nl", "pl", "pt", "ru", "sv", "sw", "te", "th", "tr", "vi", "zh"], "license": "apache-2.0", "tags": ["luke", "named entity recognition", "relation classification", "question answering"], "thumbnail": "https://git... | studio-ousia/mluke-large-lite | null | [
"transformers",
"pytorch",
"luke",
"fill-mask",
"named entity recognition",
"relation classification",
"question answering",
"multilingual",
"ar",
"bn",
"de",
"el",
"en",
"es",
"fi",
"fr",
"hi",
"id",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"sv",
"sw",
"te",
... | null | 2022-04-13T09:48:26+00:00 | [
"2010.01057"
] | [
"multilingual",
"ar",
"bn",
"de",
"el",
"en",
"es",
"fi",
"fr",
"hi",
"id",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"sv",
"sw",
"te",
"th",
"tr",
"vi",
"zh"
] | TAGS
#transformers #pytorch #luke #fill-mask #named entity recognition #relation classification #question answering #multilingual #ar #bn #de #el #en #es #fi #fr #hi #id #it #ja #ko #nl #pl #pt #ru #sv #sw #te #th #tr #vi #zh #arxiv-2010.01057 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## mLUKE
mLUKE (multilingual LUKE) is a multilingual extension of LUKE.
Please check the official repository for
more details and updates.
This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number
of parameters in this model is 561M.
The model was initialized with the weights of XLM-RoBE... | [
"## mLUKE\n\nmLUKE (multilingual LUKE) is a multilingual extension of LUKE.\n\nPlease check the official repository for\nmore details and updates.\n\nThis is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number\nof parameters in this model is 561M.\nThe model was initialized with the weight... | [
"TAGS\n#transformers #pytorch #luke #fill-mask #named entity recognition #relation classification #question answering #multilingual #ar #bn #de #el #en #es #fi #fr #hi #id #it #ja #ko #nl #pl #pt #ru #sv #sw #te #th #tr #vi #zh #arxiv-2010.01057 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #regio... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# all-distilroberta-v1-finetuned-DIT-10_epochs
This model is a fine-tuned version of [sentence-transformers/all-distilroberta-v1](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "all-distilroberta-v1-finetuned-DIT-10_epochs", "results": []}]} | veddm/all-distilroberta-v1-finetuned-DIT-10_epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T10:19:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| all-distilroberta-v1-finetuned-DIT-10\_epochs
=============================================
This model is a fine-tuned version of sentence-transformers/all-distilroberta-v1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0044
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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... | luquesky/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 | 2022-04-13T10:25:59+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: 0.2155
* Accuracy: 0.934
* F1: 0.9338
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: 10",
"### Train... | [
"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... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-buddhist-sanskrit
The best performing model of the research described in the paper 'Embeddings models for Buddhist San... | {"tags": ["Buddhist Sanskrit", "BERT", {"name": "bert-base-buddhist-sanskrit"}]} | Matej/bert-base-buddhist-sanskrit | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T10:37:54+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-buddhist-sanskrit
The best performing model of the research described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (Link to the paper will be added after
the publication of conference proceedings).
## Model description
The model has the bert-base architecture and conf... | [
"# bert-base-buddhist-sanskrit\n\nThe best performing model of the research described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (Link to the paper will be added after \nthe publication of conference proceedings).",
"## Model description\n\nThe model has the bert-base architectu... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-buddhist-sanskrit\n\nThe best performing model of the research described in the paper 'Embeddings models for Buddhist Sanskrit' published at LREC 2022 (Link to the paper will be added after \nth... |
text2text-generation | transformers |
# fake-news-detector-t5
This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned [T5](https://huggingface.co/t5-base) allows to recognize fake news and misinformation.
Automaticall... | {"language": ["en"], "license": "mit", "tags": ["Cometrain AutoCode", "Cometrain AlphaML"], "datasets": ["fake-and-real-news-dataset"], "widget": [{"text": "Former FBI Agent: We've never been to the moon", "example_title": "Apollo program misinformation"}, {"text": "Finland to make decision on NATO membership in coming... | cometrain/fake-news-detector-t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Cometrain AutoCode",
"Cometrain AlphaML",
"en",
"dataset:fake-and-real-news-dataset",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T10:39:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #Cometrain AutoCode #Cometrain AlphaML #en #dataset-fake-and-real-news-dataset #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# fake-news-detector-t5
This model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned T5 allows to recognize fake news and misinformation.
Automatically trained on Fake and real news da... | [
"# fake-news-detector-t5\nThis model has been automatically fine-tuned and tested as part of the development of the GPT-2-based AutoML framework for accelerated and easy development of NLP enterprise solutions. Fine-tuned T5 allows to recognize fake news and misinformation.\nAutomatically trained on Fake and real n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #Cometrain AutoCode #Cometrain AlphaML #en #dataset-fake-and-real-news-dataset #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# fake-news-detector-t5\nThis model has been automatically fine-tuned and test... |
automatic-speech-recognition | transformers |
# Automatic Speech Recognition for Belarusian language
Fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on `mozilla-foundation/common_voice_8_0 be` dataset.
`Train`, `Dev`, `Test` splits were used as they are present in the dataset. No additional data was used from `Valid... | {"language": ["be"], "license": "gpl-3.0", "tags": ["audio", "speech", "automatic-speech-recognition"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "da... | ales/wav2vec2-cv-be | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"be",
"dataset:mozilla-foundation/common_voice_8_0",
"license:gpl-3.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T10:42:20+00:00 | [] | [
"be"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #be #dataset-mozilla-foundation/common_voice_8_0 #license-gpl-3.0 #model-index #endpoints_compatible #has_space #region-us
|
# Automatic Speech Recognition for Belarusian language
Fine-tuned version of facebook/wav2vec2-base on 'mozilla-foundation/common_voice_8_0 be' dataset.
'Train', 'Dev', 'Test' splits were used as they are present in the dataset. No additional data was used from 'Validated' split,
only 1 voicing of each sentence was... | [
"# Automatic Speech Recognition for Belarusian language\n\nFine-tuned version of facebook/wav2vec2-base on 'mozilla-foundation/common_voice_8_0 be' dataset.\n\n'Train', 'Dev', 'Test' splits were used as they are present in the dataset. No additional data was used from 'Validated' split, \nonly 1 voicing of each sen... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #be #dataset-mozilla-foundation/common_voice_8_0 #license-gpl-3.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Automatic Speech Recognition for Belarusian language\n\nFine-tuned version of facebook/wav2vec2-ba... |
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. -->
# javilonso/classificationEsp1_Augmented_Polarity
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationEsp1_Augmented_Polarity", "results": []}]} | javilonso/classificationEsp1_Augmented_Polarity | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T10:49:54+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/classificationEsp1\_Augmented\_Polarity
=================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1633
* Validation Loss: 0.6795
* Epoch: 2
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11565, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #roberta #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': 'AdamWeightDecay', 'learning\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLMv2-L12-H384-distilled-finetuned-clinc
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoB... | {"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "me... | philschmid/MiniLMv2-L12-H384-distilled-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T10:56:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-distilled-finetuned-clinc
===========================================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3058
* Accuracy: 0.9529
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #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: 0.0001\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-multilingual-cased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-multilingual-cased](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-multilingual-cased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type... | Toshifumi/distilbert-base-multilingual-cased-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 | 2022-04-13T11:15:27+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-multilingual-cased-finetuned-emotion
====================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3702
* Accuracy: 0.8885
* F1: 0.8888
Model d... | [
"### 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... |
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. -->
# twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS
This model is a fine-tuned version of [cardiffnlp/twitte... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS", "results": []}]} | lucaordronneau/twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T11:29:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE\_SENTENCE-NEWS
========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
* L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# thesis-freeform-yesno
This model is a fine-tuned version of [maretamasaeva/thesis-freeform](https://huggingface.co/maretamasaeva... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "thesis-freeform-yesno", "results": []}]} | maretamasaeva/thesis-freeform-yesno | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T11:34:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| thesis-freeform-yesno
=====================
This model is a fine-tuned version of maretamasaeva/thesis-freeform on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4547
* Accuracy: 0.0194
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### 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* num\\_epochs: 4",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #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\\_si... |
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-cnndm_wikihow_test_on_cnndm
This model is a fine-tuned version of [Chikashi/t5-small-finetuned-cnndm-wikihow]... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-cnndm_wikihow_test_on_cnndm", "results": []}]} | Chikashi/t5-small-finetuned-cnndm_wikihow_test_on_cnndm | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T12:26:02+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-cnndm_wikihow_test_on_cnndm
This model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm-wikihow on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# t5-small-finetuned-cnndm_wikihow_test_on_cnndm\n\nThis model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm-wikihow on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-cnndm_wikihow_test_on_cnndm\n\nThis model is a fine-tuned version of Chikashi/t5-small-finetuned-cnndm-wi... |
translation | transformers | # opus-mt-tc-big-en-bg
Neural machine translation model for translating from English (en) to Bulgarian (bg).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mo... | {"language": ["bg", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-bg", "results": [{"task": {"type": "translation", "name": "Translation eng-bul"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng bul devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-bg | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T12:29:41+00:00 | [] | [
"bg",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-bg
====================
Neural machine translation model for translating from English (en) to Bulgarian (bg).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tr... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# bert-base-multilingual-cased-finetuned-emotion
This model is a fine-tuned version of [bert-base-multilingual-cased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "em... | Toshifumi/bert-base-multilingual-cased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T12:33:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-multilingual-cased-finetuned-emotion
==============================================
This model is a fine-tuned version of bert-base-multilingual-cased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2369
* Accuracy: 0.9195
* F1: 0.9205
Model description
-------... | [
"### 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 #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
translation | transformers | # opus-mt-tc-big-en-ar
Neural machine translation model for translating from English (en) to Arabic (ar).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | {"language": ["ar", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-ar", "results": [{"task": {"type": "translation", "name": "Translation eng-ara"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng ara devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-ar | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ar",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T12:38:54+00:00 | [] | [
"ar",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ar #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-ar
====================
Neural machine translation model for translating from English (en) to Arabic (ar).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally train... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ar #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-cat_oci_spa
Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible fo... | {"language": ["ca", "en", "es", "oc"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-cat_oci_spa", "results": [{"task": {"type": "translation", "name": "Translation eng-cat"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng cat dev... | Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ca",
"en",
"es",
"oc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T12:40:56+00:00 | [] | [
"ca",
"en",
"es",
"oc"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ca #en #es #oc #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-cat\_oci\_spa
===============================
Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many la... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ca #en #es #oc #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-jeffbezos-sweatystartup | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T12:46:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Jeff Bezos & Nick Huber
@elonmusk-jeffbezos-sweatystartup
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, ch... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-ces_slk
Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in ... | {"language": ["ces", "slk", "cs", "sk", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-ces_slk", "results": [{"task": {"type": "translation", "name": "Translation eng-ces"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng ces... | Helsinki-NLP/opus-mt-tc-big-en-ces_slk | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ces",
"slk",
"cs",
"sk",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T12:46:59+00:00 | [] | [
"ces",
"slk",
"cs",
"sk",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ces #slk #cs #sk #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-ces\_slk
==========================
Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All ... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #ces #slk #cs #sk #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-el
Neural machine translation model for translating from English (en) to Modern Greek (1453-) (el).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the wo... | {"language": ["el", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-el", "results": [{"task": {"type": "translation", "name": "Translation eng-ell"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng ell devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-el | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"el",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T12:53:07+00:00 | [] | [
"el",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #el #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-el
====================
Neural machine translation model for translating from English (en) to Modern Greek (1453-) (el).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are or... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #el #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# ABrinkmann/sbert_xtremedistil-l6-h256-uncased-mean-cosine-h32
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Tr... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ABrinkmann/sbert_xtremedistil-l6-h256-uncased-mean-cosine-h32 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T12:54:18+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ABrinkmann/sbert_xtremedistil-l6-h256-uncased-mean-cosine-h32
This is a sentence-transformers model: It maps sentences & paragraphs to a 32 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 se... | [
"# ABrinkmann/sbert_xtremedistil-l6-h256-uncased-mean-cosine-h32\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 32 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 y... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ABrinkmann/sbert_xtremedistil-l6-h256-uncased-mean-cosine-h32\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 32 dimensional dense vector space and can be us... |
translation | transformers | # opus-mt-tc-big-en-et
Neural machine translation model for translating from English (en) to Estonian (et).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mod... | {"language": ["en", "et"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-et", "results": [{"task": {"type": "translation", "name": "Translation eng-est"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng est devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-et | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"et",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:00:27+00:00 | [] | [
"en",
"et"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #et #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-et
====================
Neural machine translation model for translating from English (en) to Estonian (et).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tra... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #et #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-fr
Neural machine translation model for translating from English (en) to French (fr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | {"language": ["en", "fr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-fr", "results": [{"task": {"type": "translation", "name": "Translation eng-fra"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng fra devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-fr | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"fr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:07:14+00:00 | [] | [
"en",
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #fr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-fr
====================
Neural machine translation model for translating from English (en) to French (fr).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally train... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #fr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-gmq
Neural machine translation model for translating from English (en) to North Germanic languages (gmq).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in ... | {"language": ["da", "en", "fo", "gmq", "is", "nb", "nn", false, "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-gmq", "results": [{"task": {"type": "translation", "name": "Translation eng-dan"}, "dataset": {"name": "flores101-devtest", "type": "flores_10... | Helsinki-NLP/opus-mt-tc-big-en-gmq | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"en",
"gmq",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:14:55+00:00 | [] | [
"da",
"en",
"fo",
"gmq",
"is",
"nb",
"nn",
"no",
"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #en #gmq #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-gmq
=====================
Neural machine translation model for translating from English (en) to North Germanic languages (gmq).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #en #gmq #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# javilonso/classificationEsp1_TitleWithOpinion_Polarity
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/classificationEsp1_TitleWithOpinion_Polarity", "results": []}]} | javilonso/classificationEsp1_TitleWithOpinion_Polarity | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T13:18:17+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/classificationEsp1\_TitleWithOpinion\_Polarity
========================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1603
* Validation Loss: 0.6678
* Epoc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 8979, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #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': 'AdamWeightDecay', 'learning\... |
translation | transformers | # opus-mt-tc-big-en-hu
Neural machine translation model for translating from English (en) to Hungarian (hu).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mo... | {"language": ["en", "hu"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-hu", "results": [{"task": {"type": "translation", "name": "Translation eng-hun"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng hun devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-hu | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"hu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:21:29+00:00 | [] | [
"en",
"hu"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #hu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-hu
====================
Neural machine translation model for translating from English (en) to Hungarian (hu).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tr... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #hu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers |
# electra-base for QA
## Overview
**Language model:** electra-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.c... | {"license": "cc-by-4.0", "datasets": ["squad_v2"]} | bhadresh-savani/electra-base-squad2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"electra",
"question-answering",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T13:25:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #safetensors #electra #question-answering #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# electra-base for QA
## Overview
Language model: electra-base
Language: English
Downstream-task: Extractive QA
Training data: SQuAD 2.0
Eval data: SQuAD 2.0
Code: See example in FARM
Infrastructure: 1x Tesla v100
## Hyperparameters
## Performance
Evaluated on the SQuAD 2.0 dev set with the official ... | [
"# electra-base for QA",
"## Overview\nLanguage model: electra-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEval data: SQuAD 2.0 \nCode: See example in FARM \nInfrastructure: 1x Tesla v100",
"## Hyperparameters",
"## Performance\nEvaluated on the SQuAD 2.0 dev ... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #electra #question-answering #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# electra-base for QA",
"## Overview\nLanguage model: electra-base \nLanguage: English \nDownstream-task: Extractive QA \nTraining data: SQuAD 2.0 \nEv... |
translation | transformers | # opus-mt-tc-big-en-it
Neural machine translation model for translating from English (en) to Italian (it).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "it"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-it", "results": [{"task": {"type": "translation", "name": "Translation eng-ita"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng ita devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-it | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"it",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:27:31+00:00 | [] | [
"en",
"it"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #it #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-it
====================
Neural machine translation model for translating from English (en) to Italian (it).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #it #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-lv
Neural machine translation model for translating from English (en) to Latvian (lv).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "lv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-lv", "results": [{"task": {"type": "translation", "name": "Translation eng-lav"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng lav devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-lv | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"lv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:36:12+00:00 | [] | [
"en",
"lv"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-lv
====================
Neural machine translation model for translating from English (en) to Latvian (lv).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-lt
Neural machine translation model for translating from English (en) to Lithuanian (lt).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["en", "lt"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-lt", "results": [{"task": {"type": "translation", "name": "Translation eng-lit"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng lit devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-lt | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"lt",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:42:47+00:00 | [] | [
"en",
"lt"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-lt
====================
Neural machine translation model for translating from English (en) to Lithuanian (lt).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally t... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-pt
Neural machine translation model for translating from English (en) to Portuguese (pt).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["en", "pt", "pt_br"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-pt", "results": [{"task": {"type": "translation", "name": "Translation eng-por"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng por devtest"}, "met... | Helsinki-NLP/opus-mt-tc-big-en-pt | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"en",
"pt",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:49:04+00:00 | [] | [
"en",
"pt",
"pt_br"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #en #pt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-pt
====================
Neural machine translation model for translating from English (en) to Portuguese (pt).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally t... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #en #pt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-ro
Neural machine translation model for translating from English (en) to Romanian (ro).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mod... | {"language": ["en", "ro"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-ro", "results": [{"task": {"type": "translation", "name": "Translation eng-ron"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng ron devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-ro | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ro",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T13:55:54+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-ro
====================
Neural machine translation model for translating from English (en) to Romanian (ro).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tra... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #ro #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-es
Neural machine translation model for translating from English (en) to Spanish (es).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "es"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-es", "results": [{"task": {"type": "translation", "name": "Translation eng-spa"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng spa devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-es | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"es",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:04:03+00:00 | [] | [
"en",
"es"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #es #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-es
====================
Neural machine translation model for translating from English (en) to Spanish (es).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #es #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-en-tr
Neural machine translation model for translating from English (en) to Turkish (tr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "tr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-en-tr", "results": [{"task": {"type": "translation", "name": "Translation eng-tur"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "eng tur devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-en-tr | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"tr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:11:47+00:00 | [] | [
"en",
"tr"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #en #tr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-en-tr
====================
Neural machine translation model for translating from English (en) to Turkish (tr).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #en #tr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-ar-en
Neural machine translation model for translating from Arabic (ar) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | {"language": ["ar", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-ar-en", "results": [{"task": {"type": "translation", "name": "Translation ara-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ara eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-ar-en | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ar",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:18:06+00:00 | [] | [
"ar",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #ar #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-ar-en
====================
Neural machine translation model for translating from Arabic (ar) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally train... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #ar #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# results-yelp
This model is a fine-tuned version of [textattack/bert-base-uncased-yelp-polarity](https://huggingface.co/textattac... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "results-yelp", "results": []}]} | potatobunny/results-yelp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T14:20:19+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# results-yelp
This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
It achieves the following results on the evaluation set:
- Loss: 0.3563
- Accuracy: 0.9302
- Precision: 0.9461
- Recall: 0.9608
- F1: 0... | [
"# results-yelp\n\nThis model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3563\n- Accuracy: 0.9302\n- Precision: 0.9461\n- Recall: 0.960... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# results-yelp\n\nThis model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant re... |
translation | transformers | # opus-mt-tc-big-bg-en
Neural machine translation model for translating from Bulgarian (bg) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mo... | {"language": ["bg", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-bg-en", "results": [{"task": {"type": "translation", "name": "Translation bul-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "bul eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-bg-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:24:05+00:00 | [] | [
"bg",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-bg-en
====================
Neural machine translation model for translating from Bulgarian (bg) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tr... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #bg #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-cat_oci_spa-en
Neural machine translation model for translating from Catalan, Occitan and Spanish (cat+oci+spa) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible fo... | {"language": ["ca", "en", "es", "oc"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-cat_oci_spa-en", "results": [{"task": {"type": "translation", "name": "Translation cat-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "cat eng dev... | Helsinki-NLP/opus-mt-tc-big-cat_oci_spa-en | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ca",
"en",
"es",
"oc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:30:47+00:00 | [] | [
"ca",
"en",
"es",
"oc"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #ca #en #es #oc #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-cat\_oci\_spa-en
===============================
Neural machine translation model for translating from Catalan, Occitan and Spanish (cat+oci+spa) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many la... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #ca #en #es #oc #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-cel-en
Neural machine translation model for translating from Celtic languages (cel) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the worl... | {"language": ["br", "cel", "cy", "en", "ga"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-cel-en", "results": [{"task": {"type": "translation", "name": "Translation cym-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "cym eng devt... | Helsinki-NLP/opus-mt-tc-big-cel-en | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"br",
"cel",
"cy",
"en",
"ga",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:36:34+00:00 | [] | [
"br",
"cel",
"cy",
"en",
"ga"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #br #cel #cy #en #ga #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-cel-en
=====================
Neural machine translation model for translating from Celtic languages (cel) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are ori... | [] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #translation #opus-mt-tc #br #cel #cy #en #ga #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-ces_slk-en
Neural machine translation model for translating from Czech and Slovak (ces+slk) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in ... | {"language": ["cs", "en", "sk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-ces_slk-en", "results": [{"task": {"type": "translation", "name": "Translation ces-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ces eng devtest"}, "m... | Helsinki-NLP/opus-mt-tc-big-ces_slk-en | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"cs",
"en",
"sk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:42:34+00:00 | [] | [
"cs",
"en",
"sk"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #cs #en #sk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-ces\_slk-en
==========================
Neural machine translation model for translating from Czech and Slovak (ces+slk) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All ... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #cs #en #sk #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `popcornell/clarity21_train_enh_beamformer_mvdr`
This model was trained by popcornell using clarity recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/clarity/enh_2021
./run.sh --skip_data_prep false --skip_tr... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["clarity"]} | popcornell/clarity21_train_enh_beamformer_mvdr | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:clarity",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-04-13T14:43:46+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #audio-to-audio #dataset-clarity #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
| ESPnet2 ENH model
-----------------
### 'popcornell/clarity21\_train\_enh\_beamformer\_mvdr'
This model was trained by popcornell using clarity recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Apr 12 20:54:54 UTC 2022'
* python version: '3.9.7 (default... | [
"### 'popcornell/clarity21\\_train\\_enh\\_beamformer\\_mvdr'\n\n\nThis model was trained by popcornell using clarity recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Apr 12 20:54:54 UTC 2022'\n* python version: '3.9.7 (default, Sep 16 20... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-clarity #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"### 'popcornell/clarity21\\_train\\_enh\\_beamformer\\_mvdr'\n\n\nThis model was trained by popcornell using clarity recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\... |
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. -->
# vdsouza1/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vdsouza1/bert-finetuned-ner", "results": []}]} | vdsouza1/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T14:47:32+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| vdsouza1/bert-finetuned-ner
===========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0253
* Validation Loss: 0.0587
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-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: {'inner\\_optimizer': {'class\\_name': '... |
translation | transformers | # opus-mt-tc-big-el-en
Neural machine translation model for translating from Modern Greek (1453-) (el) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the wo... | {"language": ["el", "en"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-el-en", "results": [{"task": {"type": "translation", "name": "Translation ell-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ell eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-el-en | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"el",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:48:44+00:00 | [] | [
"el",
"en"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #el #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-el-en
====================
Neural machine translation model for translating from Modern Greek (1453-) (el) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are or... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #el #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-et-en
Neural machine translation model for translating from Estonian (et) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mod... | {"language": ["en", "et"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-et-en", "results": [{"task": {"type": "translation", "name": "Translation est-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "est eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-et-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"et",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T14:54:21+00:00 | [] | [
"en",
"et"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #et #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-et-en
====================
Neural machine translation model for translating from Estonian (et) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tra... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #et #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-fr-en
Neural machine translation model for translating from French (fr) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | {"language": ["en", "fr"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-fr-en", "results": [{"task": {"type": "translation", "name": "Translation fra-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "fra eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-fr-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"fr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:02:39+00:00 | [] | [
"en",
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #fr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-fr-en
====================
Neural machine translation model for translating from French (fr) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally train... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #fr #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-gmq-en
Neural machine translation model for translating from North Germanic languages (gmq) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in ... | {"language": ["da", "en", "fo", "gmq", "is", "nb", "nn", false, "sv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-gmq-en", "results": [{"task": {"type": "translation", "name": "Translation dan-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_10... | Helsinki-NLP/opus-mt-tc-big-gmq-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"gmq",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:13:21+00:00 | [] | [
"da",
"en",
"fo",
"gmq",
"is",
"nb",
"nn",
"no",
"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #gmq #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-gmq-en
=====================
Neural machine translation model for translating from North Germanic languages (gmq) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #gmq #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# twitter-roberta-base-sentiment-latest-finetuned-FG-CONCAT_SENTENCE-H-NEWS
This model is a fine-tuned version of [cardiffnlp/twit... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "twitter-roberta-base-sentiment-latest-finetuned-FG-CONCAT_SENTENCE-H-NEWS", "results": []}]} | lucaordronneau/twitter-roberta-base-sentiment-latest-finetuned-FG-CONCAT_SENTENCE-H-NEWS | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T15:16:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-sentiment-latest-finetuned-FG-CONCAT\_SENTENCE-H-NEWS
==========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 12\n* eval... |
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/1518349985649246211/cSRb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/notthatsuperman/1650838396576/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/notthatsuperman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-13T15:18:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
NotThatSuperman
@notthatsuperman
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"
] |
translation | transformers | # opus-mt-tc-big-sh-en
Neural machine translation model for translating from Serbo-Croatian (sh) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. A... | {"language": ["bs_Latn", "en", "hr", "sh", "sr_Cyrl", "sr_Latn"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-sh-en", "results": [{"task": {"type": "translation", "name": "Translation hrv-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "a... | Helsinki-NLP/opus-mt-tc-big-sh-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"sh",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:21:20+00:00 | [] | [
"bs_Latn",
"en",
"hr",
"sh",
"sr_Cyrl",
"sr_Latn"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #sh #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-sh-en
====================
Neural machine translation model for translating from Serbo-Croatian (sh) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are original... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #tc #big #sh #en #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# roberta-large-finetuned-clinc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"},... | dbounds/roberta-large-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T15:22:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc
=============================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1594
* Accuracy: 0.9742
Model description
-----------------
More information needed
Intended uses & limi... | [
"### 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* distributed\\_type: sagemaker\\_data\\_parallel\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_b... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
translation | transformers | # opus-mt-tc-big-he-en
Neural machine translation model for translating from Hebrew (he) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | {"language": ["en", "he"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-he-en", "results": [{"task": {"type": "translation", "name": "Translation heb-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "heb eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-he-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"he",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:27:23+00:00 | [] | [
"en",
"he"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #he #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-he-en
====================
Neural machine translation model for translating from Hebrew (he) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally train... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #he #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-hu-en
Neural machine translation model for translating from Hungarian (hu) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mo... | {"language": ["en", "hu"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-hu-en", "results": [{"task": {"type": "translation", "name": "Translation hun-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "hun eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-hu-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"hu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:33:48+00:00 | [] | [
"en",
"hu"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #hu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-hu-en
====================
Neural machine translation model for translating from Hungarian (hu) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally tr... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #hu #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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. -->
# MiniLMv2-L12-H384-distilled-finetuned-clinc
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoB... | {"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "config": "plus", "s... | lewtun/MiniLMv2-L12-H384-distilled-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T15:37:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-distilled-finetuned-clinc
===========================================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3058
* Accuracy: 0.9529
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #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: 0.0001\n*... |
translation | transformers | # opus-mt-tc-big-it-en
Neural machine translation model for translating from Italian (it) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "it"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-it-en", "results": [{"task": {"type": "translation", "name": "Translation ita-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ita eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-it-en | null | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"it",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:40:18+00:00 | [] | [
"en",
"it"
] | TAGS
#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #en #it #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-it-en
====================
Neural machine translation model for translating from Italian (it) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #marian #text2text-generation #translation #opus-mt-tc #en #it #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
translation | transformers | # opus-mt-tc-big-lv-en
Neural machine translation model for translating from Latvian (lv) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mode... | {"language": ["en", "lv"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-lv-en", "results": [{"task": {"type": "translation", "name": "Translation lav-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "lav eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-lv-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"lv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:46:59+00:00 | [] | [
"en",
"lv"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-lv-en
====================
Neural machine translation model for translating from Latvian (lv) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trai... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lv #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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": ["f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "... | flood/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 | 2022-04-13T15:54:25+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: 0.1698
* Accuracy : 0.933
* F1: 0.9335
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: 32\n* eval\\_batch\\_size: 32\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... |
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. -->
# javilonso/Mex_Rbta_TitleWithOpinion_Polarity
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "javilonso/Mex_Rbta_TitleWithOpinion_Polarity", "results": []}]} | javilonso/Mex_Rbta_TitleWithOpinion_Polarity | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-13T15:55:39+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| javilonso/Mex\_Rbta\_TitleWithOpinion\_Polarity
===============================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3691
* Validation Loss: 0.5035
* Epoch: 1
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 5986, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #roberta #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': 'AdamWeightDecay', 'learning\... |
translation | transformers | # opus-mt-tc-big-lt-en
Neural machine translation model for translating from Lithuanian (lt) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | {"language": ["en", "lt"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-big-lt-en", "results": [{"task": {"type": "translation", "name": "Translation lit-eng"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "lit eng devtest"}, "metrics": [{... | Helsinki-NLP/opus-mt-tc-big-lt-en | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"lt",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-13T15:56:01+00:00 | [] | [
"en",
"lt"
] | TAGS
#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| opus-mt-tc-big-lt-en
====================
Neural machine translation model for translating from Lithuanian (lt) to English (en).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally t... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #marian #text2text-generation #translation #opus-mt-tc #en #lt #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.