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fill-mask | transformers |
# PaloBERT
## Model description
A Greek language model based on [RoBERTa](https://arxiv.org/abs/1907.11692)
## Training data
The training data is a corpus of 458,293 documents collected from Greek social media accounts. It also contains a GTP-2 tokenizer trained from scratch on the same corpus.
The training corpu... | {"language": "el"} | gealexandri/palobert-base-greek-uncased-v1 | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"el",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"el"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #el #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# PaloBERT
## Model description
A Greek language model based on RoBERTa
## Training data
The training data is a corpus of 458,293 documents collected from Greek social media accounts. It also contains a GTP-2 tokenizer trained from scratch on the same corpus.
The training corpus has been collected and provided by... | [
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"## Model description\n\nA Greek language model based on RoBERTa",
"## Training data\n\nThe training data is a corpus of 458,293 documents collected from Greek social media accounts. It also contains a GTP-2 tokenizer trained from scratch on the same corpus.\n\nThe training corpus has been collect... | [
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"## Model description\n\nA Greek language model based on RoBERTa",
"## Training data\n\nThe training data is a corpus of 458,293 documents collected from Greek ... |
feature-extraction | transformers | hello
| {} | geekfeed/gpt2_ja | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #feature-extraction #endpoints_compatible #text-generation-inference #region-us
| hello
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null | null | https://dl.fbaipublicfiles.com/avhubert/model/lrs3_vox/vsr/base_vox_433h.pt | {} | g30rv17ys/avhubert | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
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fill-mask | transformers |
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking Fix MLM Parameters
Init parameters by https://huggingface.co/hfl/chinese-roberta-wwm-ext-large
miss mlm parameters issue https://github.com/ymcui/Chinese-BERT-wwm/issues/98
Only train MLM parameters and freeze other pa... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | genggui001/chinese_roberta_wwm_large_ext_fix_mlm | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking Fix MLM Parameters
Init parameters by URL
miss mlm parameters issue URL
Only train MLM parameters and freeze other parameters
More info in github URL
| [
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"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking Fix MLM Parameters\n\nInit parameters by URL\n\nmiss mlm ... |
automatic-speech-recognition | transformers |
# xls-asr-vi-40h-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on 40 hours of FPT Open Speech Dataset (FOSD) and Common Voice 7.0.
### Benchmark WER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://hugg... | {"language": ["vi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common-voice", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "xls-asr-vi-40h-1B", "results": [{"task": {"type": "automatic-speech-recognition", "name": "S... | geninhu/xls-asr-vi-40h-1B | null | [
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"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
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"vi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
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| xls-asr-vi-40h-1B
=================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on 40 hours of FPT Open Speech Dataset (FOSD) and Common Voice 7.0.
### Benchmark WER result:
### Benchmark CER result:
Evaluation
----------
Please use the URL file to run the evaluation
Training procedur... | [
"### Benchmark WER result:",
"### Benchmark CER result:\n\n\n\nEvaluation\n----------\n\n\nPlease use the URL file to run the evaluation\n\n\nTraining procedure\n------------------",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* trai... | [
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"### Benchmark WER result:",
"### Benchmark CER result... |
automatic-speech-recognition | transformers |
# xls-asr-vi-40h
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common voice 7.0 vi & private dataset.
It achieves the following results on the evaluation set (Without Language Model):
- Loss: 1.1177
- Wer: 60.58
## Evaluation
Please r... | {"language": ["vi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common-voice", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "xls-asr-vi-40h", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spee... | geninhu/xls-asr-vi-40h | null | [
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"pytorch",
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"vi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
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| xls-asr-vi-40h
==============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common voice 7.0 vi & private dataset.
It achieves the following results on the evaluation set (Without Language Model):
* Loss: 1.1177
* Wer: 60.58
Evaluation
----------
Please run the URL file
Training pr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\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:... | [
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"### Training hyperparameters\n\n\nThe following hyperpa... |
text-generation | transformers | # MechDistilGPT2
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Environmental Impact](#environmental-impact)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- ... | {"tags": ["Causal Language modeling", "text-generation", "CLM"], "model_index": [{"name": "MechDistilGPT2", "results": [{"task": {"name": "Causal Language modeling", "type": "Causal Language modeling"}}]}]} | geralt/MechDistilGPT2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"Causal Language modeling",
"CLM",
"arxiv:2105.09680",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.09680",
"1910.09700"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #Causal Language modeling #CLM #arxiv-2105.09680 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # MechDistilGPT2
## Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Environmental Impact
- How to Get Started With the Model
## Model Details
- Model Description:
This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books.
- Developed by: Ashwin
- Mode... | [
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"## Table of Contents\n- Model Details \n- Uses\n- Risks, Limitations and Biases\n- Training\n- Environmental Impact\n- How to Get Started With the Model",
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"# MechDistilGPT2",
"## Table of Contents\n- Model Details \n- Uses\n- Risks, Limitations and Biases\n- T... |
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. -->
# biobert_v1.1_pubmed-finetuned-squad
This model is a fine-tuned version of [gerardozq/biobert_v1.1_pubmed-finetuned-squad](https:... | {"tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "biobert_v1.1_pubmed-finetuned-squad", "results": []}]} | gerardozq/biobert_v1.1_pubmed-finetuned-squad | null | [
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"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad_v2 #endpoints_compatible #region-us
|
# biobert_v1.1_pubmed-finetuned-squad
This model is a fine-tuned version of gerardozq/biobert_v1.1_pubmed-finetuned-squad on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tra... | [
"# biobert_v1.1_pubmed-finetuned-squad\n\nThis model is a fine-tuned version of gerardozq/biobert_v1.1_pubmed-finetuned-squad on the squad_v2 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore info... | [
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"## Model ... |
null | transformers |
# German Electra Uncased
<img width="300px" src="https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/german-electra-logo.png">
[¹]
## Version 2 Release
We released an improved version of this model. Version 1 was trained for 766,000 steps. For this new version we continue... | {"language": "de", "license": "mit", "tags": ["electra", "commoncrawl", "uncased", "umlaute", "umlauts", "german", "deutsch"], "thumbnail": "https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/german-electra-logo.png"} | german-nlp-group/electra-base-german-uncased | null | [
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"electra",
"pretraining",
"commoncrawl",
"uncased",
"umlaute",
"umlauts",
"german",
"deutsch",
"de",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #electra #pretraining #commoncrawl #uncased #umlaute #umlauts #german #deutsch #de #license-mit #endpoints_compatible #region-us
|
# German Electra Uncased
<img width="300px" src="URL
[¹]
## Version 2 Release
We released an improved version of this model. Version 1 was trained for 766,000 steps. For this new version we continued the training for an additional 734,000 steps. It therefore follows that version 2 was trained on a total of 1,500,000 ... | [
"# German Electra Uncased\n<img width=\"300px\" src=\"URL\n[¹]",
"## Version 2 Release\nWe released an improved version of this model. Version 1 was trained for 766,000 steps. For this new version we continued the training for an additional 734,000 steps. It therefore follows that version 2 was trained on a total... | [
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"# German Electra Uncased\n<img width=\"300px\" src=\"URL\n[¹]",
"## Version 2 Release\nWe released an improved version of this model. Version 1 was... |
fill-mask | transformers |
# SlovakBERT (base-sized model)
SlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-sensitive: it makes a difference between slovensko and Slovensko.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly i... | {"language": "sk", "license": "mit", "tags": ["SlovakBERT"], "datasets": ["wikipedia", "opensubtitles", "oscar", "gerulatawebcrawl", "gerulatamonitoring", "blbec.online"]} | gerulata/slovakbert | null | [
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"tf",
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"SlovakBERT",
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"dataset:gerulatawebcrawl",
"dataset:gerulatamonitoring",
"dataset:blbec.online",
"arxiv:2109.15254",
"license:mit",
"autotrain_c... | null | 2022-03-02T23:29:05+00:00 | [
"2109.15254"
] | [
"sk"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #fill-mask #SlovakBERT #sk #dataset-wikipedia #dataset-opensubtitles #dataset-oscar #dataset-gerulatawebcrawl #dataset-gerulatamonitoring #dataset-blbec.online #arxiv-2109.15254 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# SlovakBERT (base-sized model)
SlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-sensitive: it makes a difference between slovensko and Slovensko.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly i... | [
"# SlovakBERT (base-sized model)\nSlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-sensitive: it makes a difference between slovensko and Slovensko.",
"## Intended uses & limitations\nYou can use the raw model for masked language modeling, but it'... | [
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text-generation | transformers |
# Family Guy (Peter) DialoGPT Model | {"tags": ["conversational"]} | gfdream/dialogpt-small-familyguy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Family Guy (Peter) DialoGPT Model | [
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] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | gfdream/dialogpt-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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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-herblabels
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-herblabels", "results": []}]} | ggosline/t5-small-herblabels | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-herblabels
===================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4823
* Rouge1: 3.0759
* Rouge2: 1.0495
* Rougel: 3.0758
* Rougelsum: 3.0431
* Gen Len: 18.9716
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
null | adapter-transformers |
# Adapter `ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR` for ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR
An [adapter](https://adapterhub.ml) for the `ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-fro... | {"tags": ["adapter-transformers", "adapterhub:other", "xlm-roberta"], "datasets": ["ghadeermobasher/BC5CDR-Chemical-Disease"]} | ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR | null | [
"adapter-transformers",
"pytorch",
"xlm-roberta",
"adapterhub:other",
"dataset:ghadeermobasher/BC5CDR-Chemical-Disease",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#adapter-transformers #pytorch #xlm-roberta #adapterhub-other #dataset-ghadeermobasher/BC5CDR-Chemical-Disease #region-us
|
# Adapter 'ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR' for ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR
An adapter for the 'ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR' model that was tr... | [
"# Adapter 'ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR' for ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR\n\nAn adapter for the 'ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR' model that ... | [
"TAGS\n#adapter-transformers #pytorch #xlm-roberta #adapterhub-other #dataset-ghadeermobasher/BC5CDR-Chemical-Disease #region-us \n",
"# Adapter 'ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-lang-from-XLMR' for ghadeermobasher/BC5CDR-Chemical-Disease-balanced-SapBERT-UMLS-2020AB-all-la... |
text-classification | transformers | A fake news detector using RoBERTa.
Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
Training involved using hyperparameter search with 10 trials. | {} | ghanashyamvtatti/roberta-fake-news | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| A fake news detector using RoBERTa.
Dataset: URL
Training involved using hyperparameter search with 10 trials. | [] | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | This repository belongs to TransportersBERT from ActTrans publication.
Taju, Semmy Wellem, Syed Muazzam Ali Shah, and Yu-Yen Ou. “ActTRANS: Functional Classification in Active Transport Proteins Based on Transfer Learning and Contextual Representations.” Computational Biology and Chemistry 93 (August 1, 2021): 107537.... | {} | ghazikhanihamed/TransportersBERT | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| This repository belongs to TransportersBERT from ActTrans publication.
Taju, Semmy Wellem, Syed Muazzam Ali Shah, and Yu-Yen Ou. “ActTRANS: Functional Classification in Active Transport Proteins Based on Transfer Learning and Contextual Representations.” Computational Biology and Chemistry 93 (August 1, 2021): 107537.... | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Connor | {"tags": ["conversational"]} | ghhostboy/DialoGPT-medium-connorDBH3-1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Connor | [
"# Connor"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Connor"
] |
text-generation | transformers |
# Connor | {"tags": ["conversational"]} | ghhostboy/DialoGPT-medium-connorDBH3-21 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Connor | [
"# Connor"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Connor"
] |
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. -->
# common6
This model is a fine-tuned version of [common6/checkpoint-3500](https://huggingface.co/common6/checkpoint-3500) on the C... | {"language": ["fa"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "common6", "results": []}]} | ghofrani/common6 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"fa",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #fa #dataset-common_voice #endpoints_compatible #region-us
| common6
=======
This model is a fine-tuned version of common6/checkpoint-3500 on the COMMON\_VOICE - FA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3706
* Wer: 0.3421
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-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_s... |
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. -->
# common7
This model is a fine-tuned version of [common7/checkpoint-18500](https://huggingface.co/common7/checkpoint-18500) on the... | {"language": ["fa"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "common7", "results": []}]} | ghofrani/common7 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"fa",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #fa #dataset-common_voice #endpoints_compatible #region-us
| common7
=======
This model is a fine-tuned version of common7/checkpoint-18500 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - FA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3448
* Wer: 0.3478
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #fa #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-... |
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. -->
# common8
This model is a fine-tuned version of [wghts/checkpoint-20000](https://huggingface.co/wghts/checkpoint-20000) on the MOZ... | {"language": ["fa"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "common8", "results": []}]} | ghofrani/xls-r-1b-fa-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"fa",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #fa #dataset-common_voice #endpoints_compatible #region-us
| common8
=======
This model is a fine-tuned version of wghts/checkpoint-20000 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - FA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3174
* Wer: 0.3022
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 6\n* total\\_train\\_batch\\_size: 192\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #fa #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-... |
text-generation | transformers | # Bangla-GPT2
### A GPT-2 Model for the Bengali Language
* Dataset- mc4 Bengali
* Training time- ~40 hours
* Written in- JAX
If you use this model, please cite:
```
@misc{bangla-gpt2,
author = {Ritobrata Ghosh},
year = {2016},
title = {Bangla GPT-2},
publisher = {Hugging Face}
}
``` | {"language": "bn", "tags": ["text-generation"], "widget": [{"text": "\u0986\u099c \u098f\u0995\u099f\u09bf \u09b8\u09c1\u09a8\u09cd\u09a6\u09b0 \u09a6\u09bf\u09a8 \u098f\u09ac\u0982 \u0986\u09ae\u09bf"}]} | ritog/bangla-gpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"bn",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #bn #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Bangla-GPT2
### A GPT-2 Model for the Bengali Language
* Dataset- mc4 Bengali
* Training time- ~40 hours
* Written in- JAX
If you use this model, please cite:
| [
"# Bangla-GPT2",
"### A GPT-2 Model for the Bengali Language\n\n* Dataset- mc4 Bengali\n* Training time- ~40 hours\n* Written in- JAX\n\nIf you use this model, please cite:"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #bn #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bangla-GPT2",
"### A GPT-2 Model for the Bengali Language\n\n* Dataset- mc4 Bengali\n* Training time- ~40 hours\n* Written in- JAX\n\nIf you use this model, ple... |
text-generation | transformers | # Robi Kobi
### Created by [Ritobrata Ghosh](https://ghosh-r.github.io)
A model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore.
This model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the [Bangla GPT-2](https://huggingface.co/ghosh-r/bangla-gpt... | {"language": "bn", "tags": ["text-generation"], "widget": [{"text": "\u09a4\u09cb\u09ae\u09be\u0995\u09c7 \u09a6\u09c7\u0996\u09c7\u099b\u09bf \u0986\u09ae\u09be\u09b0 \u09b9\u09c3\u09a6\u09df \u09ae\u09be\u099d\u09c7"}]} | ritog/robi-kobi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"bn",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #bn #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Robi Kobi
### Created by Ritobrata Ghosh
A model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore.
This model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the Bangla GPT-2 pretrained model, trained on mc4-Bengali dataset. | [
"# Robi Kobi",
"### Created by Ritobrata Ghosh\n\nA model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore.\n\nThis model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the Bangla GPT-2 pretrained model, trained on mc4-Bengali dataset."
] | [
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"# Robi Kobi",
"### Created by Ritobrata Ghosh\n\nA model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore.\n\nThis model is fine-tuned... |
automatic-speech-recognition | transformers |
You can test this model online with the [**Space for Romanian Speech Recognition**](https://huggingface.co/spaces/gigant/romanian-speech-recognition)
The model ranked **TOP-1** on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge :
* [**The 🤗 Speech Bench**](https://huggingface.co/spaces/hugg... | {"language": ["ro"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0", "gigant/romanian_speech_synthesis_0_8_1"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-ro-300m_01"... | gigant/romanian-wav2vec2 | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
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"dataset:mozilla-foundation/common_voice_8_0",
"dataset:gigant/romanian_speech_synthesis_0_8_1",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ro #dataset-mozilla-foundation/common_voice_8_0 #dataset-gigant/romanian_speech_synthesis_0_8_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_... | You can test this model online with the Space for Romanian Speech Recognition
The model ranked TOP-1 on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge :
* The Speech Bench
* Speech Challenge Leaderboard
Romanian Wav2Vec2
=================
This model is a fine-tuned version of facebook/... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 3\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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fill-mask | transformers | # StackOBERTflow-comments-small
StackOBERTflow is a RoBERTa model trained on StackOverflow comments.
A Byte-level BPE tokenizer with dropout was used (using the `tokenizers` package).
The model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens.
The model was trained for ... | {} | giganticode/StackOBERTflow-comments-small-v1 | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # StackOBERTflow-comments-small
StackOBERTflow is a RoBERTa model trained on StackOverflow comments.
A Byte-level BPE tokenizer with dropout was used (using the 'tokenizers' package).
The model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens.
The model was trained for ... | [
"# StackOBERTflow-comments-small\n\nStackOBERTflow is a RoBERTa model trained on StackOverflow comments.\nA Byte-level BPE tokenizer with dropout was used (using the 'tokenizers' package).\n\nThe model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens. \nThe model was t... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# StackOBERTflow-comments-small\n\nStackOBERTflow is a RoBERTa model trained on StackOverflow comments.\nA Byte-level BPE tokenizer with dropout was used (using the 'tokenizers' package).\n\nThe mod... |
token-classification | transformers |
## About
The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on
[github](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'camembert-base'*.
## Supported Tags
... | {"language": "fr", "widget": [{"text": "Face \u00e0 un choc in\u00e9dit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des m\u00e9nages"}]} | gilf/french-camembert-postag-model | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"token-classification",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #camembert #token-classification #fr #autotrain_compatible #endpoints_compatible #region-us
| About
-----
The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on
github. The base tokenizer and model used for training is *'camembert-base'*.
Supported Tags
--------------
It uses the following tags:
More informati... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #camembert #token-classification #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
<figure>
| Hyperparameter | Value |
|-----... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]} | gilparmentier/pokemon_gptj_model | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.09864",
"2101.00027"
] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| GPT-J 6B
========
Model Description
-----------------
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
**\*** Each layer consists of one feedforward block and one self attention block.
... | [
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unkn... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Bias... |
text-generation | transformers |
# Jake Peralta DialoGPT model | {"tags": ["conversational"]} | gizmo-dev/DialoGPT-small-jake | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jake Peralta DialoGPT model | [
"# Jake Peralta DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jake Peralta DialoGPT model"
] |
null | transformers | # cse_resnet50
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tri... | {} | glasses/cse_resnet50 | null | [
"transformers",
"pytorch",
"arxiv:1512.03385",
"arxiv:1812.01187",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #arxiv-1512.03385 #arxiv-1812.01187 #endpoints_compatible #region-us
| # cse_resnet50
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# cse_resnet50\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-1512.03385 #arxiv-1812.01187 #endpoints_compatible #region-us \n",
"# cse_resnet50\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
null | transformers | # deit_base_patch16_224
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://githu... | {} | glasses/deit_base_patch16_224 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # deit_base_patch16_224
Implementation of DeiT proposed in Training data-efficient image
transformers & distillation through
attention
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
!image
| [
"# deit_base_patch16_224\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the model, the [dist]{.title-ref} token.\n\n !image"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# deit_base_patch16_224\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the mo... |
null | transformers | # deit_base_patch16_384
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://githu... | {} | glasses/deit_base_patch16_384 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # deit_base_patch16_384
Implementation of DeiT proposed in Training data-efficient image
transformers & distillation through
attention
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
!image
| [
"# deit_base_patch16_384\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the model, the [dist]{.title-ref} token.\n\n !image"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# deit_base_patch16_384\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the mo... |
null | transformers | # deit_small_patch16_224
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://gith... | {} | glasses/deit_small_patch16_224 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # deit_small_patch16_224
Implementation of DeiT proposed in Training data-efficient image
transformers & distillation through
attention
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
!image
| [
"# deit_small_patch16_224\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the model, the [dist]{.title-ref} token.\n\n !image"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# deit_small_patch16_224\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the m... |
null | transformers | # deit_tiny_patch16_224
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://githu... | {} | glasses/deit_tiny_patch16_224 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # deit_tiny_patch16_224
Implementation of DeiT proposed in Training data-efficient image
transformers & distillation through
attention
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
!image
| [
"# deit_tiny_patch16_224\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the model, the [dist]{.title-ref} token.\n\n !image"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# deit_tiny_patch16_224\n Implementation of DeiT proposed in Training data-efficient image\n transformers & distillation through\n attention\n\n An attention based distillation is proposed where a new token is added\n to the mo... |
null | transformers | # densenet161
Implementation of DenseNet proposed in [Densely Connected Convolutional
Networks](https://arxiv.org/abs/1608.06993)
Create a default models
``` {.sourceCode .}
DenseNet.densenet121()
DenseNet.densenet161()
DenseNet.densenet169()
DenseNet.densenet201()
```
Examples:
``` {.sourceCode .}
# ch... | {} | glasses/densenet161 | null | [
"transformers",
"pytorch",
"arxiv:1608.06993",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1608.06993"
] | [] | TAGS
#transformers #pytorch #arxiv-1608.06993 #endpoints_compatible #region-us
| # densenet161
Implementation of DenseNet proposed in Densely Connected Convolutional
Networks
Create a default models
Examples:
| [
"# densenet161\nImplementation of DenseNet proposed in Densely Connected Convolutional\nNetworks\n\n Create a default models\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-1608.06993 #endpoints_compatible #region-us \n",
"# densenet161\nImplementation of DenseNet proposed in Densely Connected Convolutional\nNetworks\n\n Create a default models\n\n \n\n Examples:"
] |
null | transformers | # densenet169
Implementation of DenseNet proposed in [Densely Connected Convolutional
Networks](https://arxiv.org/abs/1608.06993)
Create a default models
``` {.sourceCode .}
DenseNet.densenet121()
DenseNet.densenet161()
DenseNet.densenet169()
DenseNet.densenet201()
```
Examples:
``` {.sourceCode .}
# ch... | {} | glasses/densenet169 | null | [
"transformers",
"pytorch",
"arxiv:1608.06993",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1608.06993"
] | [] | TAGS
#transformers #pytorch #arxiv-1608.06993 #endpoints_compatible #region-us
| # densenet169
Implementation of DenseNet proposed in Densely Connected Convolutional
Networks
Create a default models
Examples:
| [
"# densenet169\nImplementation of DenseNet proposed in Densely Connected Convolutional\nNetworks\n\n Create a default models\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-1608.06993 #endpoints_compatible #region-us \n",
"# densenet169\nImplementation of DenseNet proposed in Densely Connected Convolutional\nNetworks\n\n Create a default models\n\n \n\n Examples:"
] |
null | transformers | # densenet201
Implementation of DenseNet proposed in [Densely Connected Convolutional
Networks](https://arxiv.org/abs/1608.06993)
Create a default models
``` {.sourceCode .}
DenseNet.densenet121()
DenseNet.densenet161()
DenseNet.densenet169()
DenseNet.densenet201()
```
Examples:
``` {.sourceCode .}
# ch... | {} | glasses/densenet201 | null | [
"transformers",
"pytorch",
"arxiv:1608.06993",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1608.06993"
] | [] | TAGS
#transformers #pytorch #arxiv-1608.06993 #endpoints_compatible #region-us
| # densenet201
Implementation of DenseNet proposed in Densely Connected Convolutional
Networks
Create a default models
Examples:
| [
"# densenet201\nImplementation of DenseNet proposed in Densely Connected Convolutional\nNetworks\n\n Create a default models\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-1608.06993 #endpoints_compatible #region-us \n",
"# densenet201\nImplementation of DenseNet proposed in Densely Connected Convolutional\nNetworks\n\n Create a default models\n\n \n\n Examples:"
] |
null | transformers | # ResNet
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks fo... | {} | glasses/dummy | null | [
"transformers",
"pytorch",
"arxiv:1512.03385",
"arxiv:1812.01187",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #arxiv-1512.03385 #arxiv-1812.01187 #endpoints_compatible #region-us
| # ResNet
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# ResNet\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-1512.03385 #arxiv-1812.01187 #endpoints_compatible #region-us \n",
"# ResNet\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # eca_resnet26t
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tr... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/eca_resnet26t | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # eca_resnet26t
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# eca_resnet26t\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# eca_resnet26t\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
null | transformers | # efficientnet_b0
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | {} | glasses/efficientnet_b0 | null | [
"transformers",
"pytorch",
"arxiv:1905.11946",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.11946"
] | [] | TAGS
#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us
| # efficientnet_b0
Implementation of EfficientNet proposed in EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks
!image
The basic architecture is similar to MobileNetV2 as was computed by
using Progressive Neural Architecture
Search .
The following table shows the basic architecture
(Effic... | [
"# efficientnet_b0\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using Progressive Neural Architecture\n Search .\n\n The following table shows the basic arch... | [
"TAGS\n#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us \n",
"# efficientnet_b0\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using... |
null | transformers | # efficientnet_b2
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | {} | glasses/efficientnet_b2 | null | [
"transformers",
"pytorch",
"arxiv:1905.11946",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.11946"
] | [] | TAGS
#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us
| # efficientnet_b2
Implementation of EfficientNet proposed in EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks
!image
The basic architecture is similar to MobileNetV2 as was computed by
using Progressive Neural Architecture
Search .
The following table shows the basic architecture
(Effic... | [
"# efficientnet_b2\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using Progressive Neural Architecture\n Search .\n\n The following table shows the basic arch... | [
"TAGS\n#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us \n",
"# efficientnet_b2\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using... |
null | transformers | # efficientnet_b3
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | {} | glasses/efficientnet_b3 | null | [
"transformers",
"pytorch",
"arxiv:1905.11946",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.11946"
] | [] | TAGS
#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us
| # efficientnet_b3
Implementation of EfficientNet proposed in EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks
!image
The basic architecture is similar to MobileNetV2 as was computed by
using Progressive Neural Architecture
Search .
The following table shows the basic architecture
(Effic... | [
"# efficientnet_b3\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using Progressive Neural Architecture\n Search .\n\n The following table shows the basic arch... | [
"TAGS\n#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us \n",
"# efficientnet_b3\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using... |
null | transformers | # efficientnet_b6
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | {} | glasses/efficientnet_b6 | null | [
"transformers",
"pytorch",
"arxiv:1905.11946",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.11946"
] | [] | TAGS
#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us
| # efficientnet_b6
Implementation of EfficientNet proposed in EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks
!image
The basic architecture is similar to MobileNetV2 as was computed by
using Progressive Neural Architecture
Search .
The following table shows the basic architecture
(Effic... | [
"# efficientnet_b6\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using Progressive Neural Architecture\n Search .\n\n The following table shows the basic arch... | [
"TAGS\n#transformers #pytorch #arxiv-1905.11946 #endpoints_compatible #region-us \n",
"# efficientnet_b6\nImplementation of EfficientNet proposed in EfficientNet: Rethinking\nModel Scaling for Convolutional Neural\nNetworks\n\n !image\n\n The basic architecture is similar to MobileNetV2 as was computed by\n using... |
null | transformers | # regnetx_002
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnetx_002 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnetx_002
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnetx_002\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnetx_002\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
null | transformers | # regnetx_006
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnetx_006 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnetx_006
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnetx_006\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnetx_006\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
null | transformers | # regnetx_016
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnetx_016 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnetx_016
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnetx_016\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnetx_016\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
null | transformers | # regnety_002
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnety_002 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnety_002
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnety_002\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnety_002\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
null | transformers | # regnety_004
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnety_004 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnety_004
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnety_004\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnety_004\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
null | transformers | # regnety_006
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnety_006 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnety_006
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnety_006\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnety_006\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
null | transformers | # regnety_008
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | {} | glasses/regnety_008 | null | [
"transformers",
"pytorch",
"arxiv:2003.13678",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.13678"
] | [] | TAGS
#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us
| # regnety_008
Implementation of RegNet proposed in Designing Network Design
Spaces
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current search
space.
The resulting models a... | [
"# regnety_008\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n based on the best performing models sampled by the current search\n space.\n\n The resu... | [
"TAGS\n#transformers #pytorch #arxiv-2003.13678 #endpoints_compatible #region-us \n",
"# regnety_008\nImplementation of RegNet proposed in Designing Network Design\nSpaces\n\n The main idea is to start with a high dimensional search space and\n iteratively reduce the search space by empirically apply constrains\n... |
image-classification | transformers | # resnet152
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet152 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet152
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet152\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet152\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet18
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks ... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet18 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet18
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet18\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet18\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet26
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks ... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet26 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet26
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet26\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet26\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet26d
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet26d | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet26d
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet26d\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet26d\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet34
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks ... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet34 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet34
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet34\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet34\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet34d
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet34d | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet34d
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet34d\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet34d\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet50
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks ... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet50 | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet50
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet50\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet50\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
image-classification | transformers | # resnet50d
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]} | glasses/resnet50d | null | [
"transformers",
"pytorch",
"image-classification",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187"
] | [] | TAGS
#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us
| # resnet50d
Implementation of ResNet proposed in Deep Residual Learning for Image
Recognition
Examples:
| [
"# resnet50d\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #image-classification #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# resnet50d\nImplementation of ResNet proposed in Deep Residual Learning for Image\nRecognition\n\n \n\n Examples:"
] |
null | transformers | # resnext101_32x8d
Implementation of ResNetXt proposed in [\"Aggregated Residual
Transformation for Deep Neural
Networks\"](https://arxiv.org/pdf/1611.05431.pdf)
Create a default model
``` python
ResNetXt.resnext50_32x4d()
ResNetXt.resnext101_32x8d()
# create a resnetxt18_32x4d
ResNetXt.resnet18(block=ResNetXtB... | {} | glasses/resnext101_32x8d | null | [
"transformers",
"pytorch",
"arxiv:1611.05431",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1611.05431"
] | [] | TAGS
#transformers #pytorch #arxiv-1611.05431 #endpoints_compatible #region-us
| # resnext101_32x8d
Implementation of ResNetXt proposed in \"Aggregated Residual
Transformation for Deep Neural
Networks\"
Create a default model
Examples:
:
| [
"# resnext101_32x8d\nImplementation of ResNetXt proposed in \\\"Aggregated Residual\nTransformation for Deep Neural\nNetworks\\\"\n\n Create a default model\n\n \n\n Examples:\n\n :"
] | [
"TAGS\n#transformers #pytorch #arxiv-1611.05431 #endpoints_compatible #region-us \n",
"# resnext101_32x8d\nImplementation of ResNetXt proposed in \\\"Aggregated Residual\nTransformation for Deep Neural\nNetworks\\\"\n\n Create a default model\n\n \n\n Examples:\n\n :"
] |
null | transformers | # resnext50_32x4d
Implementation of ResNetXt proposed in [\"Aggregated Residual
Transformation for Deep Neural
Networks\"](https://arxiv.org/pdf/1611.05431.pdf)
Create a default model
``` python
ResNetXt.resnext50_32x4d()
ResNetXt.resnext101_32x8d()
# create a resnetxt18_32x4d
ResNetXt.resnet18(block=ResNetXtBo... | {} | glasses/resnext50_32x4d | null | [
"transformers",
"pytorch",
"arxiv:1611.05431",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1611.05431"
] | [] | TAGS
#transformers #pytorch #arxiv-1611.05431 #endpoints_compatible #region-us
| # resnext50_32x4d
Implementation of ResNetXt proposed in \"Aggregated Residual
Transformation for Deep Neural
Networks\"
Create a default model
Examples:
:
| [
"# resnext50_32x4d\nImplementation of ResNetXt proposed in \\\"Aggregated Residual\nTransformation for Deep Neural\nNetworks\\\"\n\n Create a default model\n\n \n\n Examples:\n\n :"
] | [
"TAGS\n#transformers #pytorch #arxiv-1611.05431 #endpoints_compatible #region-us \n",
"# resnext50_32x4d\nImplementation of ResNetXt proposed in \\\"Aggregated Residual\nTransformation for Deep Neural\nNetworks\\\"\n\n Create a default model\n\n \n\n Examples:\n\n :"
] |
null | transformers | # vgg11
Implementation of VGG proposed in [Very Deep Convolutional Networks For
Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
``` python
VGG.vgg11()
VGG.vgg13()
VGG.vgg16()
VGG.vgg19()
VGG.vgg11_bn()
VGG.vgg13_bn()
VGG.vgg16_bn()
VGG.vgg19_bn()
```
Please be aware that the [bn]{.title... | {} | glasses/vgg11 | null | [
"transformers",
"pytorch",
"arxiv:1409.1556",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1409.1556"
] | [] | TAGS
#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us
| # vgg11
Implementation of VGG proposed in Very Deep Convolutional Networks For
Large-Scale Image Recognition
Please be aware that the [bn]{.title-ref} models uses BatchNorm but
they are very old and people back then don\'t know the bias is
superfluous in a conv followed by a batchnorm.
Examples:
| [
"# vgg11\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back then don\\'t know the bias is\n superfluous in a conv followed by a batchnorm.\n\n Examples... | [
"TAGS\n#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us \n",
"# vgg11\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back then... |
null | transformers | # vgg11_bn
Implementation of VGG proposed in [Very Deep Convolutional Networks For
Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
``` python
VGG.vgg11()
VGG.vgg13()
VGG.vgg16()
VGG.vgg19()
VGG.vgg11_bn()
VGG.vgg13_bn()
VGG.vgg16_bn()
VGG.vgg19_bn()
```
Please be aware that the [bn]{.ti... | {} | glasses/vgg11_bn | null | [
"transformers",
"pytorch",
"arxiv:1409.1556",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1409.1556"
] | [] | TAGS
#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us
| # vgg11_bn
Implementation of VGG proposed in Very Deep Convolutional Networks For
Large-Scale Image Recognition
Please be aware that the [bn]{.title-ref} models uses BatchNorm but
they are very old and people back then don\'t know the bias is
superfluous in a conv followed by a batchnorm.
Examples:
| [
"# vgg11_bn\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back then don\\'t know the bias is\n superfluous in a conv followed by a batchnorm.\n\n Examp... | [
"TAGS\n#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us \n",
"# vgg11_bn\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back t... |
null | transformers | # vgg13_bn
Implementation of VGG proposed in [Very Deep Convolutional Networks For
Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
``` python
VGG.vgg11()
VGG.vgg13()
VGG.vgg16()
VGG.vgg19()
VGG.vgg11_bn()
VGG.vgg13_bn()
VGG.vgg16_bn()
VGG.vgg19_bn()
```
Please be aware that the [bn]{.ti... | {} | glasses/vgg13_bn | null | [
"transformers",
"pytorch",
"arxiv:1409.1556",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1409.1556"
] | [] | TAGS
#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us
| # vgg13_bn
Implementation of VGG proposed in Very Deep Convolutional Networks For
Large-Scale Image Recognition
Please be aware that the [bn]{.title-ref} models uses BatchNorm but
they are very old and people back then don\'t know the bias is
superfluous in a conv followed by a batchnorm.
Examples:
| [
"# vgg13_bn\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back then don\\'t know the bias is\n superfluous in a conv followed by a batchnorm.\n\n Examp... | [
"TAGS\n#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us \n",
"# vgg13_bn\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back t... |
null | transformers | # vgg19_bn
Implementation of VGG proposed in [Very Deep Convolutional Networks For
Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
``` python
VGG.vgg11()
VGG.vgg13()
VGG.vgg16()
VGG.vgg19()
VGG.vgg11_bn()
VGG.vgg13_bn()
VGG.vgg16_bn()
VGG.vgg19_bn()
```
Please be aware that the [bn]{.ti... | {} | glasses/vgg19_bn | null | [
"transformers",
"pytorch",
"arxiv:1409.1556",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1409.1556"
] | [] | TAGS
#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us
| # vgg19_bn
Implementation of VGG proposed in Very Deep Convolutional Networks For
Large-Scale Image Recognition
Please be aware that the [bn]{.title-ref} models uses BatchNorm but
they are very old and people back then don\'t know the bias is
superfluous in a conv followed by a batchnorm.
Examples:
| [
"# vgg19_bn\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back then don\\'t know the bias is\n superfluous in a conv followed by a batchnorm.\n\n Examp... | [
"TAGS\n#transformers #pytorch #arxiv-1409.1556 #endpoints_compatible #region-us \n",
"# vgg19_bn\nImplementation of VGG proposed in Very Deep Convolutional Networks For\nLarge-Scale Image Recognition\n\n \n\n Please be aware that the [bn]{.title-ref} models uses BatchNorm but\n they are very old and people back t... |
null | transformers | # vit_base_patch16_224
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/glas... | {} | glasses/vit_base_patch16_224 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # vit_base_patch16_224
Implementation of Vision Transformer (ViT) proposed in An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale
The following image from the authors shows the architecture.
!image
Examples:
| [
"# vit_base_patch16_224\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n !image\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# vit_base_patch16_224\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n... |
null | transformers | # vit_base_patch16_384
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/glas... | {} | glasses/vit_base_patch16_384 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # vit_base_patch16_384
Implementation of Vision Transformer (ViT) proposed in An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale
The following image from the authors shows the architecture.
!image
Examples:
| [
"# vit_base_patch16_384\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n !image\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# vit_base_patch16_384\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n... |
null | transformers | # vit_huge_patch16_224
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/glas... | {} | glasses/vit_huge_patch16_224 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # vit_huge_patch16_224
Implementation of Vision Transformer (ViT) proposed in An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale
The following image from the authors shows the architecture.
!image
Examples:
| [
"# vit_huge_patch16_224\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n !image\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# vit_huge_patch16_224\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n... |
null | transformers | # vit_huge_patch32_384
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/glas... | {} | glasses/vit_huge_patch32_384 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # vit_huge_patch32_384
Implementation of Vision Transformer (ViT) proposed in An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale
The following image from the authors shows the architecture.
!image
Examples:
| [
"# vit_huge_patch32_384\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n !image\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# vit_huge_patch32_384\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n... |
null | transformers | # vit_large_patch16_224
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/gla... | {} | glasses/vit_large_patch16_224 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # vit_large_patch16_224
Implementation of Vision Transformer (ViT) proposed in An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale
The following image from the authors shows the architecture.
!image
Examples:
| [
"# vit_large_patch16_224\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n !image\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# vit_large_patch16_224\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\... |
null | transformers | # vit_large_patch16_384
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/gla... | {} | glasses/vit_large_patch16_384 | null | [
"transformers",
"pytorch",
"arxiv:2010.11929",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929"
] | [] | TAGS
#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us
| # vit_large_patch16_384
Implementation of Vision Transformer (ViT) proposed in An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale
The following image from the authors shows the architecture.
!image
Examples:
| [
"# vit_large_patch16_384\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\n !image\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-2010.11929 #endpoints_compatible #region-us \n",
"# vit_large_patch16_384\n Implementation of Vision Transformer (ViT) proposed in An Image Is\n Worth 16x16 Words: Transformers For Image Recognition At\n Scale\n\n The following image from the authors shows the architecture.\n\... |
null | transformers | # wide_resnet101_2
Implementation of Wide ResNet proposed in [\"Wide Residual
Networks\"](https://arxiv.org/pdf/1605.07146.pdf)
Create a default model
``` python
WideResNet.wide_resnet50_2()
WideResNet.wide_resnet101_2()
# create a wide_resnet18_4
WideResNet.resnet18(block=WideResNetBottleNeckBlock, width_facto... | {} | glasses/wide_resnet101_2 | null | [
"transformers",
"pytorch",
"arxiv:1605.07146",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1605.07146"
] | [] | TAGS
#transformers #pytorch #arxiv-1605.07146 #endpoints_compatible #region-us
| # wide_resnet101_2
Implementation of Wide ResNet proposed in \"Wide Residual
Networks\"
Create a default model
Examples:
| [
"# wide_resnet101_2\nImplementation of Wide ResNet proposed in \\\"Wide Residual\nNetworks\\\"\n\n Create a default model\n\n \n\n Examples:"
] | [
"TAGS\n#transformers #pytorch #arxiv-1605.07146 #endpoints_compatible #region-us \n",
"# wide_resnet101_2\nImplementation of Wide ResNet proposed in \\\"Wide Residual\nNetworks\\\"\n\n Create a default model\n\n \n\n Examples:"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-spanish-custom
This model was trained from scratch on the common_voice dataset.
It achieves the follow... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-custom", "results": []}]} | glob-asr/base-spanish-asr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-spanish-custom
This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: ... | [
"# wav2vec2-large-xls-r-300m-spanish-custom\n\nThis model was trained from scratch on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2245\n- eval_wer: 0.2082\n- eval_runtime: 801.6784\n- eval_samples_per_second: 18.822\n- eval_steps_per_second: 2.354\n- epoch: 0.... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-spanish-custom\n\nThis model was trained from scratch on the common_voice dataset.\nIt achieves the following results on the evalua... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-guarani-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"language": ["gn"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "gn", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-guarani-small", "results": []}]} | glob-asr/wav2vec2-large-xls-r-300m-guarani-small | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"gn",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #gn #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-guarani-small
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4964
* Wer: 0.5957
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #gn #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-spanish-small
This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-cus... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-small", "results": []}]} | glob-asr/wav2vec2-large-xls-r-300m-spanish-small | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-spanish-small
=======================================
This model is a fine-tuned version of jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3596
* Wer: 0.2105
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-spanish-large
This model is a fine-tuned version of [tomascufaro/xls-r-es-test](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "es", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-large", "results": []}]} | glob-asr/wav2vec2-xls-r-300m-spanish-large-LM | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"es",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #es #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-spanish-large
=======================================
This model is a fine-tuned version of tomascufaro/xls-r-es-test on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1431
* Wer: 0.1197
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 20\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #es #robust-speech-event #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* lea... |
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. -->
# xls-r-es-test-lm
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec... | {"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "es", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-es-test-lm", "results": [{"task... | glob-asr/xls-r-es-test-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #es #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| xls-r-es-test-lm
================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ES dataset.
It achieves the following results on the test set with lm model:
* Loss: 0.1304
* WER: 0.094
* CER: 0.031
It achieves the following results on the val... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #es #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Traini... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Romanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Romanian using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model ca... | {"language": "ro", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "XLSR Wav2Vec2 Romanian by George Mihaila", "results": [{"task": {"type": "automatic-s... | gmihaila/wav2vec2-large-xlsr-53-romanian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ro",
"dataset:common_voice",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ro #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Romanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Romanian using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as... | [
"# Wav2Vec2-Large-XLSR-53-Romanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Romanian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ro #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Romanian\n\nFine-tuned facebook/wa... |
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. -->
# BERiTmodel2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "BERiTmodel2", "results": []}]} | gngpostalsrvc/BERiTmodel2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BERiTmodel2
===========
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1508
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* ev... |
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. -->
# mt5-small-finetuned-xsum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "def... | gniemiec/mt5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-xsum
========================
This model is a fine-tuned version of google/mt5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 2.8351
* Rouge2: 0.3143
* Rougel: 2.6488
* Rougelsum: 2.6463
* Gen Len: 4.9416
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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "defa... | gniemiec/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7967
* Rouge1: 23.0533
* Rouge2: 3.912
* Rougel: 17.8534
* Rougelsum: 17.8581
* Gen Len: 18.6878
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
image-classification | transformers |
# diam
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | godiec/diam | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# diam
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### bunny
!bunny
#### moon
!moon
#### sun
!sun
#### tiger
!tiger | [
"# diam\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### bunny\n\n!bunny",
"#### moon\n\n!moon",
"#### sun\n\n!sun",
"#### tiger\n\n!tiger"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# diam\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the... |
feature-extraction | transformers |
## KoBART-base-v1
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
model = BartModel.from_pretrained('gogamza/kobart-base-v1')
```
| {"language": "ko", "license": "mit", "tags": ["bart"]} | gogamza/kobart-base-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"feature-extraction",
"ko",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bart #feature-extraction #ko #license-mit #endpoints_compatible #has_space #region-us
|
## KoBART-base-v1
| [
"## KoBART-base-v1"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #feature-extraction #ko #license-mit #endpoints_compatible #has_space #region-us \n",
"## KoBART-base-v1"
] |
feature-extraction | transformers |
# Model Card for kobart-base-v2
# Model Details
## Model Description
[**BART**](https://arxiv.org/pdf/1910.13461.pdf)(**B**idirectional and **A**uto-**R**egressive **T**ransformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 `autoencoder`의 형태로 학습이 됩니다. 한국어 BART(이하 **KoBART**) 는 논문에서 사용된 `Text Infilling` 노이즈 함수를 사용... | {"language": "ko", "license": "mit", "tags": ["bart"]} | gogamza/kobart-base-v2 | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"feature-extraction",
"ko",
"arxiv:1910.13461",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461",
"1910.09700"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bart #feature-extraction #ko #arxiv-1910.13461 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us
| Model Card for kobart-base-v2
=============================
Model Details
=============
Model Description
-----------------
BART(Bidirectional and Auto-Regressive Transformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 'autoencoder'의 형태로 학습이 됩니다. 한국어 BART(이하 KoBART) 는 논문에서 사용된 'Text Infilling' 노이즈 함수를 사용하여 40GB 이상의 한... | [
"### Tokenizer\n\n\n'tokenizers' 패키지의 'Character BPE tokenizer'로 학습되었습니다.",
"### Speeds, Sizes, Times\n\n\n\nEvaluation\n==========\n\n\nTesting Data, Factors & Metrics\n-------------------------------",
"### Testing Data\n\n\nMore information needed",
"### Factors\n\n\nMore information needed",
"### Metric... | [
"TAGS\n#transformers #pytorch #safetensors #bart #feature-extraction #ko #arxiv-1910.13461 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us \n",
"### Tokenizer\n\n\n'tokenizers' 패키지의 'Character BPE tokenizer'로 학습되었습니다.",
"### Speeds, Sizes, Times\n\n\n\nEvaluation\n==========\n\n\nTesting Data, F... |
text2text-generation | transformers |
# Korean News Summarization Model
## Demo
https://huggingface.co/spaces/gogamza/kobart-summarization
## How to use
```python
import torch
from transformers import PreTrainedTokenizerFast
from transformers import BartForConditionalGeneration
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-summa... | {"language": "ko", "license": "mit", "tags": ["bart"]} | gogamza/kobart-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #ko #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Korean News Summarization Model
## Demo
URL
## How to use
| [
"# Korean News Summarization Model",
"## Demo\n\nURL",
"## How to use"
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"# Korean News Summarization Model",
"## Demo\n\nURL",
"## How to use"
] |
null | transformers | Please refer : https://github.com/haven-jeon/LegalQA#train | {} | gogamza/kobert-legalqa-v1 | null | [
"transformers",
"pytorch",
"bert",
"next-sentence-prediction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #next-sentence-prediction #endpoints_compatible #region-us
| Please refer : URL | [] | [
"TAGS\n#transformers #pytorch #bert #next-sentence-prediction #endpoints_compatible #region-us \n"
] |
translation | transformers | Byt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
| {"language": ["ain", "ja"], "tags": ["translation"]} | Language-Media-Lab/byt5-small-ain-jpn-mt | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"ain",
"ja",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ain",
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #ain #ja #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Byt5-small-ain-jpn-mt is a machine translation model pretrained with Google's ByT5-small and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #ain #ja #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | Byt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
| {"language": ["jpn", "ain"], "tags": ["translation"]} | Language-Media-Lab/byt5-small-jpn-ain-mt | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"jpn",
"ain",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jpn",
"ain"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #translation #jpn #ain #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Byt5-small-jpn-ain-mt is a machine translation model pretrained with Google's ByT5-small and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #translation #jpn #ain #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | mt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
| {"language": ["jpn", "ain"], "tags": ["translation"]} | Language-Media-Lab/mt5-small-ain-jpn-mt | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"translation",
"jpn",
"ain",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jpn",
"ain"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #translation #jpn #ain #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-ain-jpn-mt is a machine translation model pretrained with Google's mT5-small and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
| [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #jpn #ain #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
translation | transformers | mt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
| {"language": ["jpn", "ain"], "tags": ["translation"]} | Language-Media-Lab/mt5-small-jpn-ain-mt | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"translation",
"jpn",
"ain",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"jpn",
"ain"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #translation #jpn #ain #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-jpn-ain-mt is a machine translation model pretrained with Google's mT5-small and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
| [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #jpn #ain #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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": []}]} | gokulkarthik/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 | 2022-03-02T23:29:05+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... |
question-answering | transformers | # XLM-RoBERTa for question answering in Indian languages
pre-trained XLM-Roberta with intermediate pre-training on SQUAD dataset (English) and fine tuning on Chaii dataset (Tamil, Hindi)
# How to use from the 🤗/transformers library
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenize... | {"language": ["en", "ta", "hi"], "datasets": ["squad", "chaii"], "widget": [{"text": "\u0b85\u0bb2\u0bc1\u0bae\u0bbf\u0ba9\u0bbf\u0baf\u0ba4\u0bcd\u0ba4\u0bbf\u0ba9\u0bcd \u0b85\u0ba3\u0bc1 \u0b8e\u0ba3\u0bcd \u0b8e\u0ba9\u0bcd\u0ba9?", "context": "\u0b85\u0bb2\u0bc1\u0bae\u0bbf\u0ba9\u0bbf\u0baf\u0bae\u0bcd (\u0b86\u0... | gokulkarthik/xlm-roberta-qa-chaii | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ta",
"hi",
"dataset:squad",
"dataset:chaii",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"ta",
"hi"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #en #ta #hi #dataset-squad #dataset-chaii #endpoints_compatible #region-us
| # XLM-RoBERTa for question answering in Indian languages
pre-trained XLM-Roberta with intermediate pre-training on SQUAD dataset (English) and fine tuning on Chaii dataset (Tamil, Hindi)
# How to use from the /transformers library
| [
"# XLM-RoBERTa for question answering in Indian languages\npre-trained XLM-Roberta with intermediate pre-training on SQUAD dataset (English) and fine tuning on Chaii dataset (Tamil, Hindi)",
"# How to use from the /transformers library"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #en #ta #hi #dataset-squad #dataset-chaii #endpoints_compatible #region-us \n",
"# XLM-RoBERTa for question answering in Indian languages\npre-trained XLM-Roberta with intermediate pre-training on SQUAD dataset (English) and fine tuning on Chaii datas... |
text2text-generation | transformers |
# rachael-scai
Generation model (Pegasus fine-tuned with QReCC) used in the participation of group Rachael for SCAI 2021.
GitHub repository can be found in: [gonced8/rachael-scai](https://github.com/gonced8/rachael-scai)
Gonçalo Raposo
## Cite
```bibtex
@InProceedings{Raposo2022,
author = {Gonça... | {"license": "gpl-3.0"} | gonced8/pegasus-conversational-qa | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"pegasus",
"text2text-generation",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #safetensors #pegasus #text2text-generation #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# rachael-scai
Generation model (Pegasus fine-tuned with QReCC) used in the participation of group Rachael for SCAI 2021.
GitHub repository can be found in: gonced8/rachael-scai
Gonçalo Raposo
## Cite
| [
"# rachael-scai\r\nGeneration model (Pegasus fine-tuned with QReCC) used in the participation of group Rachael for SCAI 2021. \r\n\r\nGitHub repository can be found in: gonced8/rachael-scai\r\n\r\nGonçalo Raposo",
"## Cite"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #pegasus #text2text-generation #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# rachael-scai\r\nGeneration model (Pegasus fine-tuned with QReCC) used in the participation of group Rachael for SCAI 2021. \r\n\r\nGitHub repository can be ... |
translation | transformers |
# bert2bert_L-24_wmt_de_en EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/bert24_de_en/1).
The model is an encoder-decoder model that was i... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["wmt14"]} | google/bert2bert_L-24_wmt_de_en | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"translation",
"en",
"de",
"dataset:wmt14",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en",
"de"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert2bert_L-24_wmt_de_en EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'bert-large' checkpoints for both the encoder
and decoder and fine-tuned... | [
"# bert2bert_L-24_wmt_de_en EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'bert-large' checkpoints for both the encoder \nand decoder and ... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert2bert_L-24_wmt_de_en EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sasc... |
translation | transformers |
# bert2bert_L-24_wmt_en_de EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/bert24_en_de/1).
The model is an encoder-decoder model that was i... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["wmt14"]} | google/bert2bert_L-24_wmt_en_de | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"translation",
"en",
"de",
"dataset:wmt14",
"arxiv:1907.12461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12461"
] | [
"en",
"de"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert2bert_L-24_wmt_en_de EncoderDecoder model
The model was introduced in
this paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository.
The model is an encoder-decoder model that was initialized on the 'bert-large' checkpoints for both the encoder
and decoder and fine-tuned... | [
"# bert2bert_L-24_wmt_en_de EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in this repository. \n\nThe model is an encoder-decoder model that was initialized on the 'bert-large' checkpoints for both the encoder \nand decoder and ... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #translation #en #de #dataset-wmt14 #arxiv-1907.12461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert2bert_L-24_wmt_en_de EncoderDecoder model\n\nThe model was introduced in \nthis paper by Sascha Rothe, S... |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-10_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-10_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-10_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-10_H-768_A-12 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-12_H-128_A-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-12_H-256_A-4 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture ... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | google/bert_uncased_L-12_H-512_A-8 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.08962"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us
| BERT Miniatures
===============
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective)... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1908.08962 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
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