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text-classification | transformers | learning rate: 3e-5
training epochs: 5
batch size: 8
seed: 42
model: bert-base-uncased
The model is pretrained on MNLI (we use kangnichaluo/mnli-2 directly) and then finetuned on CB which is converted into two-way nli classification (predict entailment or not-entailment class) | {} | kangnichaluo/mnli-cb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| learning rate: 3e-5
training epochs: 5
batch size: 8
seed: 42
model: bert-base-uncased
The model is pretrained on MNLI (we use kangnichaluo/mnli-2 directly) and then finetuned on CB which is converted into two-way nli classification (predict entailment or not-entailment class) | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
## GlossBERT
A BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: '[GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://arxiv.org/pdf/1908.07245.pdf)'
Disclaimer: This model was... | {"language": "en", "license": "mit", "tags": ["glossbert"], "datasets": ["SemCor3.0"]} | kanishka/GlossBERT | null | [
"transformers",
"pytorch",
"bert",
"glossbert",
"en",
"dataset:SemCor3.0",
"arxiv:1908.07245",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.07245"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #glossbert #en #dataset-SemCor3.0 #arxiv-1908.07245 #license-mit #endpoints_compatible #has_space #region-us
|
## GlossBERT
A BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: 'GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge'
Disclaimer: This model was built and trained by a group of researc... | [
"## GlossBERT\n\nA BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: 'GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge'\n\nDisclaimer: This model was built and trained by a group of... | [
"TAGS\n#transformers #pytorch #bert #glossbert #en #dataset-SemCor3.0 #arxiv-1908.07245 #license-mit #endpoints_compatible #has_space #region-us \n",
"## GlossBERT\n\nA BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-CoLA-finetuned-cola
This model is a fine-tuned version of [textattack/bert-base-uncased-CoLA](https://huggingf... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-CoLA-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [... | kapilchauhan/bert-base-uncased-CoLA-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-CoLA-finetuned-cola
=====================================
This model is a fine-tuned version of textattack/bert-base-uncased-CoLA on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8318
* Matthews Correlation: 0.5755
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-CoLA-finetuned-cola
This model is a fine-tuned version of [textattack/distilbert-base-uncased-CoLA](http... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-CoLA-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metri... | kapilchauhan/distilbert-base-uncased-CoLA-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-CoLA-finetuned-cola
===========================================
This model is a fine-tuned version of textattack/distilbert-base-uncased-CoLA on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6996
* Matthews Correlation: 0.5689
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* tr... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | kapilchauhan/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7696
* Matthews Correlation: 0.5136
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic... | kapilkd13/xls-r-300m-hi-prod | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7805
* Wer: 0.4340
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "", "results": [{"task": {"type": "automatic... | kapilkd13/xls-r-hi-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #robust-speech-event #generated_from_trainer #hf-asr-leaderboard #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7346
* Wer: 1.0479
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #robust-speech-event #generated_from_trainer #hf-asr-leaderboard #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\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. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "bert-base-uncased-finetuned-squad", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]} | kaporter/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"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 #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0725
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
null | null | https://www.geogebra.org/m/cwcveget
https://www.geogebra.org/m/b8dzxk6z
https://www.geogebra.org/m/nqanttum
https://www.geogebra.org/m/pd3g8a4u
https://www.geogebra.org/m/jw8324jz
https://www.geogebra.org/m/wjbpvz5q
https://www.geogebra.org/m/qm3g3ma6
https://www.geogebra.org/m/sdajgph8
https://www.geogebra.org/m/e3ghh... | {} | katoensp/GG-12 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Hello World!
This is a dummy repository.
Can be deleted. | {} | katrin-kc/dummy2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Hello World!
This is a dummy repository.
Can be deleted. | [
"# Hello World!\n\nThis is a dummy repository.\nCan be deleted."
] | [
"TAGS\n#region-us \n",
"# Hello World!\n\nThis is a dummy repository.\nCan be deleted."
] |
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. -->
# opus-mt-en-ru-finetuned
This model is a fine-tuned version of [kazandaev/opus-mt-en-ru-finetuned](https://huggingface.co/kazanda... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ru-finetuned", "results": []}]} | kazandaev/opus-mt-en-ru-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"rust",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #rust #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ru-finetuned
=======================
This model is a fine-tuned version of kazandaev/opus-mt-en-ru-finetuned on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7763
* Bleu: 41.0065
* Gen Len: 29.7548
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 49\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #rust #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\... |
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. -->
# opus-mt-ru-en-finetuned
This model is a fine-tuned version of [kazandaev/opus-mt-ru-en-finetuned](https://huggingface.co/kazanda... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ru-en-finetuned", "results": []}]} | kazandaev/opus-mt-ru-en-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"rust",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #rust #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ru-en-finetuned
=======================
This model is a fine-tuned version of kazandaev/opus-mt-ru-en-finetuned on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0399
* Bleu: 43.5078
* Gen Len: 26.1256
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 49\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #rust #marian #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 49\n... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 18413376
- CO2 Emissions (in grams): 1.4091714704861447
## Validation Metrics
- Loss: 0.26672711968421936
- Rouge1: 61.765
- Rouge2: 52.5778
- RougeL: 61.3222
- RougeLsum: 61.1905
- Gen Len: 18.7805
## Usage
You can use cURL to access this mo... | {"language": "unk", "tags": "autonlp", "datasets": ["kbhugging/autonlp-data-text2sql"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.4091714704861447} | kbhugging/autonlp-text2sql-18413376 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:kbhugging/autonlp-data-text2sql",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-kbhugging/autonlp-data-text2sql #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 18413376
- CO2 Emissions (in grams): 1.4091714704861447
## Validation Metrics
- Loss: 0.26672711968421936
- Rouge1: 61.765
- Rouge2: 52.5778
- RougeL: 61.3222
- RougeLsum: 61.1905
- Gen Len: 18.7805
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 18413376\n- CO2 Emissions (in grams): 1.4091714704861447",
"## Validation Metrics\n\n- Loss: 0.26672711968421936\n- Rouge1: 61.765\n- Rouge2: 52.5778\n- RougeL: 61.3222\n- RougeLsum: 61.1905\n- Gen Len: 18.7805",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-kbhugging/autonlp-data-text2sql #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 18413376\n- CO2 Emiss... |
text-generation | transformers |
# DIO DialoGPT Model | {"tags": ["conversational"]} | kche0138/DialoGPT-medium-DIO | 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
|
# DIO DialoGPT Model | [
"# DIO DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DIO DialoGPT Model"
] |
null | transformers | ## References
- [koGPT2](https://github.com/SKT-AI/KoGPT2)
- [koGPT2-chatbot](https://github.com/haven-jeon/KoGPT2-chatbot) | {} | kco4776/kogpt-chat | null | [
"transformers",
"pytorch",
"gpt2",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #has_space #text-generation-inference #region-us
| ## References
- koGPT2
- koGPT2-chatbot | [
"## References\n- koGPT2\n- koGPT2-chatbot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## References\n- koGPT2\n- koGPT2-chatbot"
] |
text-classification | transformers | ## References
- [Soongsil-BERT](https://github.com/jason9693/Soongsil-BERT) | {} | kco4776/soongsil-bert-wellness | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ## References
- Soongsil-BERT | [
"## References\n- Soongsil-BERT"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## References\n- Soongsil-BERT"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola-2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args":... | kdo6301/bert-base-uncased-finetuned-cola-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-cola-2
==================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9235
* Matthews Correlation: 0.6016
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | kdo6301/bert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-cola
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9089
* Matthews Correlation: 0.5640
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
sentence-similarity | sentence-transformers |
# {vietnamese-sbert}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search on Vietnamese language.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "vietnamese"], "pipeline_tag": "sentence-similarity"} | keepitreal/vietnamese-sbert | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"vietnamese",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #vietnamese #endpoints_compatible #has_space #region-us
|
# {vietnamese-sbert}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search on Vietnamese language.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers... | [
"# {vietnamese-sbert}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search on Vietnamese language.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tr... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #vietnamese #endpoints_compatible #has_space #region-us \n",
"# {vietnamese-sbert}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used... |
fill-mask | transformers | ## albert-base-japanese-v1
日本語事前学習済みALBERTモデルです
## How to use
### ファインチューニング
このモデルはPreTrainedモデルです
基本的には各種タスク用にファインチューニングして使用されることを想定しています
### Fill-Mask
このモデルではTokenizerにSentencepieceを利用しています
そのままでは`[MASK]`トークンのあとに[余計なトークンが混入する問題](https://ken11.jp/blog/sentencepiece-tokenizer-bug)があるので、利用する際には以下のようにする必要があります ... | {"language": ["ja"], "license": "mit", "tags": ["fill-mask", "japanese", "albert"], "widget": [{"text": "2022\u5e74\u306e[MASK]\u6982\u8981"}]} | ken11/albert-base-japanese-v1 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"japanese",
"ja",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## albert-base-japanese-v1
日本語事前学習済みALBERTモデルです
## How to use
### ファインチューニング
このモデルはPreTrainedモデルです
基本的には各種タスク用にファインチューニングして使用されることを想定しています
### Fill-Mask
このモデルではTokenizerにSentencepieceを利用しています
そのままでは'[MASK]'トークンのあとに余計なトークンが混入する問題があるので、利用する際には以下のようにする必要があります
#### for PyTorch
#### for TensorFlow
## Trainin... | [
"## albert-base-japanese-v1\n日本語事前学習済みALBERTモデルです",
"## How to use",
"### ファインチューニング\nこのモデルはPreTrainedモデルです \n基本的には各種タスク用にファインチューニングして使用されることを想定しています",
"### Fill-Mask\nこのモデルではTokenizerにSentencepieceを利用しています \nそのままでは'[MASK]'トークンのあとに余計なトークンが混入する問題があるので、利用する際には以下のようにする必要があります",
"#### for PyTorch",
"#### fo... | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## albert-base-japanese-v1\n日本語事前学習済みALBERTモデルです",
"## How to use",
"### ファインチューニング\nこのモデルはPreTrainedモデルです \n基本的には各種タスク用にファインチューニングして使用されることを想定しています",
"### Fill-Mask\... |
token-classification | transformers | ## bert-japanese-ner
このモデルは日本語の固有表現抽出タスクを目的として、[京都大学 黒橋・褚・村脇研究室が公開しているBERT日本語Pretrainedモデル](https://nlp.ist.i.kyoto-u.ac.jp/?ku_bert_japanese)をベースに[ストックマーク株式会社が公開しているner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)でファインチューニングしたものです。
## How to use
このモデルはTokenizerに上述の京都大学BERT日本語Pretrained... | {"language": ["ja"], "license": "mit", "tags": ["ner", "token-classification", "japanese", "bert"]} | ken11/bert-japanese-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"ner",
"japanese",
"ja",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #token-classification #ner #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## bert-japanese-ner
このモデルは日本語の固有表現抽出タスクを目的として、京都大学 黒橋・褚・村脇研究室が公開しているBERT日本語Pretrainedモデルをベースにストックマーク株式会社が公開しているner-wikipedia-datasetでファインチューニングしたものです。
## How to use
このモデルはTokenizerに上述の京都大学BERT日本語PretrainedモデルのTokenizerを利用します。
当リポジトリにTokenizerは含まれていません。
利用する際は別途ダウンロードしてご用意ください。
また、Tokenizerとは別にJuman++とpyknp... | [
"## bert-japanese-ner\nこのモデルは日本語の固有表現抽出タスクを目的として、京都大学 黒橋・褚・村脇研究室が公開しているBERT日本語Pretrainedモデルをベースにストックマーク株式会社が公開しているner-wikipedia-datasetでファインチューニングしたものです。",
"## How to use\nこのモデルはTokenizerに上述の京都大学BERT日本語PretrainedモデルのTokenizerを利用します。 \n当リポジトリにTokenizerは含まれていません。 \n利用する際は別途ダウンロードしてご用意ください。 \n \nまた、Tokenizerとは別に... | [
"TAGS\n#transformers #pytorch #bert #token-classification #ner #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## bert-japanese-ner\nこのモデルは日本語の固有表現抽出タスクを目的として、京都大学 黒橋・褚・村脇研究室が公開しているBERT日本語Pretrainedモデルをベースにストックマーク株式会社が公開しているner-wikipedia-datasetでファインチューニングしたものです。",
"## Ho... |
translation | transformers | ## mbart-ja-en
このモデルは[facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)をベースに[JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html)でファインチューニングしたものです。
This model is based on [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) and fine-tuned with [JESC d... | {"language": ["ja", "en"], "license": "mit", "tags": ["translation", "japanese"], "widget": [{"text": "\u4eca\u65e5\u3082\u3054\u5b89\u5168\u306b"}]} | ken11/mbart-ja-en | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"translation",
"japanese",
"ja",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja",
"en"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #translation #japanese #ja #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## mbart-ja-en
このモデルはfacebook/mbart-large-cc25をベースにJESC datasetでファインチューニングしたものです。
This model is based on facebook/mbart-large-cc25 and fine-tuned with JESC dataset.
## How to use
## Training Data
I used the JESC dataset for training.
Thank you for publishing such a large dataset.
## Tokenizer
The tokenizer us... | [
"## mbart-ja-en\nこのモデルはfacebook/mbart-large-cc25をベースにJESC datasetでファインチューニングしたものです。 \nThis model is based on facebook/mbart-large-cc25 and fine-tuned with JESC dataset.",
"## How to use",
"## Training Data\nI used the JESC dataset for training. \nThank you for publishing such a large dataset.",
"## Tokenize... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #translation #japanese #ja #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## mbart-ja-en\nこのモデルはfacebook/mbart-large-cc25をベースにJESC datasetでファインチューニングしたものです。 \nThis model is based on facebook/mbart-large-cc25 and fine-tuned ... |
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": []}]} | kenlevine/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... |
null | null | This is an example of how a kenLM model can be downloaded with [PyCTCDecode](https://github.com/kensho-technologies/pyctcdecode) .
Simply run the following code:
```python
from pyctcdecode import LanguageModel
language_model = LanguageModel.load_from_hf_hub("kensho/5gram-spanish-kenLM")
```
The model was trained by... | {} | kensho/5gram-spanish-kenLM | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is an example of how a kenLM model can be downloaded with PyCTCDecode .
Simply run the following code:
The model was trained by Patrick von Platen for demonstration purposes. | [] | [
"TAGS\n#region-us \n"
] |
null | null | This is an example of how a kenLM model can be downloaded with [PyCTCDecode](https://github.com/kensho-technologies/pyctcdecode) .
Simply run the following code:
```python
from pyctcdecode import BeamSearchDecoderCTC
decoder = BeamSearchDecoderCTC.load_from_hf_hub("kensho/beamsearch_decoder_dummy")
```
The model wa... | {} | kensho/beamsearch_decoder_dummy | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is an example of how a kenLM model can be downloaded with PyCTCDecode .
Simply run the following code:
The model was created by Patrick von Platen for demonstration purposes. | [] | [
"TAGS\n#region-us \n"
] |
null | null | This is an example of how a kenLM model can be downloaded with [PyCTCDecode](https://github.com/kensho-technologies/pyctcdecode) .
Simply run the following code:
```python
from pyctcdecode import LanguageModel
language_model = LanguageModel.load_from_hf_hub("kensho/dummy_full_language_model")
```
The model was crea... | {} | kensho/dummy_full_language_model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is an example of how a kenLM model can be downloaded with PyCTCDecode .
Simply run the following code:
The model was created by Patrick von Platen for demonstration purposes. | [] | [
"TAGS\n#region-us \n"
] |
null | null | Used for testing of [`pyctcdecode`](https://github.com/kensho-technologies/pyctcdecode). | {} | kensho/testing_dummy_kenlm | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Used for testing of 'pyctcdecode'. | [] | [
"TAGS\n#region-us \n"
] |
null | keras |
## Keras Implementation of CycleGAN model using [Horse to Zebra dataset](https://www.tensorflow.org/datasets/catalog/cycle_gan#cycle_ganhorse2zebra) 🐴 -> 🦓
This repo contains the model and the notebook [to this Keras example on CycleGAN](https://keras.io/examples/generative/cyclegan/).
Full credits to: [Aakash Kum... | {"license": ["cc0-1.0"], "tags": ["gan", "computer vision", "horse to zebra"]} | keras-io/CycleGAN | null | [
"keras",
"gan",
"computer vision",
"horse to zebra",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #gan #computer vision #horse to zebra #license-cc0-1.0 #has_space #region-us
|
## Keras Implementation of CycleGAN model using Horse to Zebra dataset ->
This repo contains the model and the notebook to this Keras example on CycleGAN.
Full credits to: Aakash Kumar Nain
## Background Information
CycleGAN is a model that aims to solve the image-to-image translation problem. The goal of the im... | [
"## Keras Implementation of CycleGAN model using Horse to Zebra dataset -> \n\nThis repo contains the model and the notebook to this Keras example on CycleGAN.\n\nFull credits to: Aakash Kumar Nain",
"## Background Information \nCycleGAN is a model that aims to solve the image-to-image translation problem. The g... | [
"TAGS\n#keras #gan #computer vision #horse to zebra #license-cc0-1.0 #has_space #region-us \n",
"## Keras Implementation of CycleGAN model using Horse to Zebra dataset -> \n\nThis repo contains the model and the notebook to this Keras example on CycleGAN.\n\nFull credits to: Aakash Kumar Nain",
"## Background ... |
image-classification | generic |
## Image-Classification-using-EANet with Keras
This repo contains the model and the notebook on [Image Classification using EANet with Keras](https://keras.io/examples/vision/eanet/).
Credits: [ZhiYong Chang](https://github.com/czy00000) - Original Author
HF Contribution: [Drishti Sharma](https://huggingface.co/spa... | {"language": ["en"], "license": "apache-2.0", "library_name": "generic", "tags": ["keras", "tensorflow", "image-classification"], "metrics": ["accuracy"], "libraries": "TensorBoard", "model-index": [{"name": "Image-Classification-using-EANet", "results": [{"task": {"type": "Image-Classification-using-EANet"}, "dataset"... | keras-io/Image-Classification-using-EANet | null | [
"generic",
"tensorboard",
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"image-classification",
"en",
"license:apache-2.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#generic #tensorboard #keras #tensorflow #image-classification #en #license-apache-2.0 #model-index #has_space #region-us
|
## Image-Classification-using-EANet with Keras
This repo contains the model and the notebook on Image Classification using EANet with Keras.
Credits: ZhiYong Chang - Original Author
HF Contribution: Drishti Sharma
### Introduction
This example implements the EANet model for image classification, and demonstrat... | [
"## Image-Classification-using-EANet with Keras\n\nThis repo contains the model and the notebook on Image Classification using EANet with Keras.\n\nCredits: ZhiYong Chang - Original Author\n\nHF Contribution: Drishti Sharma",
"### Introduction\n\nThis example implements the EANet model for image classification, a... | [
"TAGS\n#generic #tensorboard #keras #tensorflow #image-classification #en #license-apache-2.0 #model-index #has_space #region-us \n",
"## Image-Classification-using-EANet with Keras\n\nThis repo contains the model and the notebook on Image Classification using EANet with Keras.\n\nCredits: ZhiYong Chang - Origina... |
tabular-classification | keras |
# TensorFlow's Gradient Boosted Trees Model for structured data classification
Use TF's Gradient Boosted Trees model in binary classification of structured data <br />
* Build a decision forests model by specifying the input feature usage.
* Implement a custom Binary Target encoder as a Keras Preprocessing layer to ... | {"license": "apache-2.0", "library_name": "keras", "tags": ["tabular-classification", "keras", "tensorflow"], "metrics": ["accuracy"], "model-index": [{"name": "TF_Decision_Trees", "results": [{"task": {"type": "structured-data-classification"}, "dataset": {"name": "Census-Income Data Set", "type": "census"}, "metrics"... | keras-io/TF_Decision_Trees | null | [
"keras",
"tensorboard",
"tabular-classification",
"tensorflow",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tensorboard #tabular-classification #tensorflow #license-apache-2.0 #model-index #region-us
|
# TensorFlow's Gradient Boosted Trees Model for structured data classification
Use TF's Gradient Boosted Trees model in binary classification of structured data <br />
* Build a decision forests model by specifying the input feature usage.
* Implement a custom Binary Target encoder as a Keras Preprocessing layer to ... | [
"# TensorFlow's Gradient Boosted Trees Model for structured data classification\n\nUse TF's Gradient Boosted Trees model in binary classification of structured data <br />\n\n* Build a decision forests model by specifying the input feature usage.\n* Implement a custom Binary Target encoder as a Keras Preprocessing ... | [
"TAGS\n#keras #tensorboard #tabular-classification #tensorflow #license-apache-2.0 #model-index #region-us \n",
"# TensorFlow's Gradient Boosted Trees Model for structured data classification\n\nUse TF's Gradient Boosted Trees model in binary classification of structured data <br />\n\n* Build a decision forests ... |
text-classification | keras |
## Keras Implementation of Bidirectional LSTMs for Sentiment Analysis on IMDB 🍿🎥
This repo contains the model and the notebook [on Bidirectional LSTMs for Sentiment Analysis on IMDB](https://keras.io/examples/nlp/bidirectional_lstm_imdb/).
Full credits to: [François Chollet](https://github.com/fchollet)
HF Co... | {"language": ["en"], "tags": ["text-classification"], "datasets": ["imdb"], "widget": [{"text": "I like that movie, but I'm not sure if it's my favorite."}]} | keras-io/bidirectional-lstm-imdb | null | [
"keras",
"text-classification",
"en",
"dataset:imdb",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#keras #text-classification #en #dataset-imdb #has_space #region-us
|
## Keras Implementation of Bidirectional LSTMs for Sentiment Analysis on IMDB
This repo contains the model and the notebook on Bidirectional LSTMs for Sentiment Analysis on IMDB.
Full credits to: François Chollet
HF Contribution: Drishti Sharma
### Metrics after 10 epochs:
- train_loss: 0.2085
- train_acc:... | [
"## Keras Implementation of Bidirectional LSTMs for Sentiment Analysis on IMDB \n\n\nThis repo contains the model and the notebook on Bidirectional LSTMs for Sentiment Analysis on IMDB.\n\nFull credits to: François Chollet\n\nHF Contribution: Drishti Sharma",
"### Metrics after 10 epochs:\n- train_loss: 0.2085\n... | [
"TAGS\n#keras #text-classification #en #dataset-imdb #has_space #region-us \n",
"## Keras Implementation of Bidirectional LSTMs for Sentiment Analysis on IMDB \n\n\nThis repo contains the model and the notebook on Bidirectional LSTMs for Sentiment Analysis on IMDB.\n\nFull credits to: François Chollet\n\nHF Cont... |
translation | keras |
## Keras Implementation of Character-level recurrent sequence-to-sequence model
This repo contains the model and the notebook [to this Keras example on Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/).
Full credits to: [fchollet](https://twitter.com/fchollet)
## Bac... | {"language": ["en", "fr"], "license": ["cc0-1.0"], "tags": ["seq2seq", "translation"]} | keras-io/char-lstm-seq2seq | null | [
"keras",
"seq2seq",
"translation",
"en",
"fr",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"fr"
] | TAGS
#keras #seq2seq #translation #en #fr #license-cc0-1.0 #has_space #region-us
|
## Keras Implementation of Character-level recurrent sequence-to-sequence model
This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.
Full credits to: fchollet
## Background Information
This example demonstrates how to implement a basic charact... | [
"## Keras Implementation of Character-level recurrent sequence-to-sequence model\n\nThis repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.\n\nFull credits to: fchollet",
"## Background Information \nThis example demonstrates how to implement a ... | [
"TAGS\n#keras #seq2seq #translation #en #fr #license-cc0-1.0 #has_space #region-us \n",
"## Keras Implementation of Character-level recurrent sequence-to-sequence model\n\nThis repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.\n\nFull credits t... |
image-to-image | keras | # Conditional Generative Adversarial Network
This repo contains the model and the notebook to [this Keras example on Conditional GAN](https://keras.io/examples/generative/conditional_gan/).
Full credits to: [Sayak Paul](https://twitter.com/RisingSayak)
# Background Information
Training a GAN conditioned on class lab... | {"library_name": "keras", "tags": ["image-to-image"]} | keras-io/conditional-gan | null | [
"keras",
"tensorboard",
"image-to-image",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tensorboard #image-to-image #has_space #region-us
| # Conditional Generative Adversarial Network
This repo contains the model and the notebook to this Keras example on Conditional GAN.
Full credits to: Sayak Paul
# Background Information
Training a GAN conditioned on class labels to generate handwritten digits.
Generative Adversarial Networks (GANs) let us generate ... | [
"# Conditional Generative Adversarial Network\nThis repo contains the model and the notebook to this Keras example on Conditional GAN.\n\nFull credits to: Sayak Paul",
"# Background Information\n\nTraining a GAN conditioned on class labels to generate handwritten digits.\n\nGenerative Adversarial Networks (GANs) ... | [
"TAGS\n#keras #tensorboard #image-to-image #has_space #region-us \n",
"# Conditional Generative Adversarial Network\nThis repo contains the model and the notebook to this Keras example on Conditional GAN.\n\nFull credits to: Sayak Paul",
"# Background Information\n\nTraining a GAN conditioned on class labels to... |
null | keras | ## Tensorflow Keras Implementation of Next-Frame Video Prediction with Convolutional LSTMs 📽️
This repo contains the models and the notebook [on How to build and train a convolutional LSTM model for next-frame video prediction](https://keras.io/examples/vision/conv_lstm/).
Full credits to [Amogh Joshi](https://githu... | {"license": "cc0-1.0", "tags": ["video-prediction", "moving-mnist", "video-to-video"]} | keras-io/conv-lstm | null | [
"keras",
"video-prediction",
"moving-mnist",
"video-to-video",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #video-prediction #moving-mnist #video-to-video #license-cc0-1.0 #has_space #region-us
| ## Tensorflow Keras Implementation of Next-Frame Video Prediction with Convolutional LSTMs ️
This repo contains the models and the notebook on How to build and train a convolutional LSTM model for next-frame video prediction.
Full credits to Amogh Joshi
## Background Information
The Convolutional LSTM architectures ... | [
"## Tensorflow Keras Implementation of Next-Frame Video Prediction with Convolutional LSTMs ️\n\nThis repo contains the models and the notebook on How to build and train a convolutional LSTM model for next-frame video prediction.\n\nFull credits to Amogh Joshi",
"## Background Information\nThe Convolutional LSTM ... | [
"TAGS\n#keras #video-prediction #moving-mnist #video-to-video #license-cc0-1.0 #has_space #region-us \n",
"## Tensorflow Keras Implementation of Next-Frame Video Prediction with Convolutional LSTMs ️\n\nThis repo contains the models and the notebook on How to build and train a convolutional LSTM model for next-fr... |
null | keras |
# ConvMixer model
The ConvMixer model is trained on Cifar10 dataset and is based on [the paper](https://arxiv.org/abs/2201.09792v1), [github](https://github.com/locuslab/convmixer).
Disclaimer : This is a demo model for Sayak Paul's keras [example](https://keras.io/examples/vision/convmixer/). Please refrain from u... | {"language": "en", "license": "apache-2.0", "tags": ["ConvMixer", "keras-io"], "datasets": ["cifar10"]} | keras-io/convmixer | null | [
"keras",
"ConvMixer",
"keras-io",
"en",
"dataset:cifar10",
"arxiv:2201.09792",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.09792",
"2010.11929"
] | [
"en"
] | TAGS
#keras #ConvMixer #keras-io #en #dataset-cifar10 #arxiv-2201.09792 #arxiv-2010.11929 #license-apache-2.0 #region-us
|
# ConvMixer model
The ConvMixer model is trained on Cifar10 dataset and is based on the paper, github.
Disclaimer : This is a demo model for Sayak Paul's keras example. Please refrain from using this model for any other purpose.
## Description
The paper uses 'patches' (square group of pixels) extracted from the i... | [
"# ConvMixer model\n\nThe ConvMixer model is trained on Cifar10 dataset and is based on the paper, github. \n\nDisclaimer : This is a demo model for Sayak Paul's keras example. Please refrain from using this model for any other purpose.",
"## Description\n\nThe paper uses 'patches' (square group of pixels) extrac... | [
"TAGS\n#keras #ConvMixer #keras-io #en #dataset-cifar10 #arxiv-2201.09792 #arxiv-2010.11929 #license-apache-2.0 #region-us \n",
"# ConvMixer model\n\nThe ConvMixer model is trained on Cifar10 dataset and is based on the paper, github. \n\nDisclaimer : This is a demo model for Sayak Paul's keras example. Please re... |
null | keras | ## Automatic Speech Recognition using CTC model on the 🤗Hub!
Full credits go to [Mohamed Reda Bouadjenek]() and [Ngoc Dung Huynh]().
This repository contains the model from [this notebook on Automatic Speech Recognition using CTC](https://keras.io/examples/audio/ctc_asr/).
| {"license": "cc0-1.0", "tags": ["speech recognition", "ctc"], "dataset": ["LJSpeech dataset"]} | keras-io/ctc_asr | null | [
"keras",
"speech recognition",
"ctc",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #speech recognition #ctc #license-cc0-1.0 #has_space #region-us
| ## Automatic Speech Recognition using CTC model on the Hub!
Full credits go to [Mohamed Reda Bouadjenek]() and [Ngoc Dung Huynh]().
This repository contains the model from this notebook on Automatic Speech Recognition using CTC.
| [
"## Automatic Speech Recognition using CTC model on the Hub! \nFull credits go to [Mohamed Reda Bouadjenek]() and [Ngoc Dung Huynh]().\n\nThis repository contains the model from this notebook on Automatic Speech Recognition using CTC."
] | [
"TAGS\n#keras #speech recognition #ctc #license-cc0-1.0 #has_space #region-us \n",
"## Automatic Speech Recognition using CTC model on the Hub! \nFull credits go to [Mohamed Reda Bouadjenek]() and [Ngoc Dung Huynh]().\n\nThis repository contains the model from this notebook on Automatic Speech Recognition using C... |
null | keras |
## Keras Implementation of Deep Deterministic Policy Gradient ⏱🤖
This repo contains the model and the notebook [to this Keras example on Deep Deterministic Policy Gradient on pendulum](https://keras.io/examples/rl/ddpg_pendulum/).
Full credits to: [Hemant Singh](https://github.com/amifunny)
![pendulum_gif](https:... | {"license": ["cc0-1.0"], "tags": ["reinforcement learning", "cartpole", "deep deterministic policy gradient"]} | keras-io/deep-deterministic-policy-gradient | null | [
"keras",
"reinforcement learning",
"cartpole",
"deep deterministic policy gradient",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #reinforcement learning #cartpole #deep deterministic policy gradient #license-cc0-1.0 #region-us
|
## Keras Implementation of Deep Deterministic Policy Gradient ⏱
This repo contains the model and the notebook to this Keras example on Deep Deterministic Policy Gradient on pendulum.
Full credits to: Hemant Singh
!pendulum_gif
## Background Information
Deep Deterministic Policy Gradient (DDPG) is a model-free of... | [
"## Keras Implementation of Deep Deterministic Policy Gradient ⏱ \nThis repo contains the model and the notebook to this Keras example on Deep Deterministic Policy Gradient on pendulum.\n\nFull credits to: Hemant Singh\n\n!pendulum_gif",
"## Background Information \nDeep Deterministic Policy Gradient (DDPG) is a ... | [
"TAGS\n#keras #reinforcement learning #cartpole #deep deterministic policy gradient #license-cc0-1.0 #region-us \n",
"## Keras Implementation of Deep Deterministic Policy Gradient ⏱ \nThis repo contains the model and the notebook to this Keras example on Deep Deterministic Policy Gradient on pendulum.\n\nFull cre... |
null | keras |
## Keras Implementation of Deep Dream 🦚🌌
This repo contains the model and the notebook [for this Deep Dream implementation of Keras](https://keras.io/examples/generative/deep_dream/).
Full credits to: [François Chollet](https://twitter.com/fchollet)
![deepdream](https://keras.io/img/examples/generative/deep_drea... | {"license": ["cc0-1.0"], "tags": ["gan", "generative adversarial networks", "deep dream"]} | keras-io/deep-dream | null | [
"keras",
"gan",
"generative adversarial networks",
"deep dream",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #gan #generative adversarial networks #deep dream #license-cc0-1.0 #has_space #region-us
|
## Keras Implementation of Deep Dream
This repo contains the model and the notebook for this Deep Dream implementation of Keras.
Full credits to: François Chollet
!deepdream
## Background Information
"Deep dream" is an image-filtering technique which consists of taking an image classification model, and runnin... | [
"## Keras Implementation of Deep Dream \n\nThis repo contains the model and the notebook for this Deep Dream implementation of Keras.\n\nFull credits to: François Chollet\n\n !deepdream",
"## Background Information\n\n \"Deep dream\" is an image-filtering technique which consists of taking an image classification... | [
"TAGS\n#keras #gan #generative adversarial networks #deep dream #license-cc0-1.0 #has_space #region-us \n",
"## Keras Implementation of Deep Dream \n\nThis repo contains the model and the notebook for this Deep Dream implementation of Keras.\n\nFull credits to: François Chollet\n\n !deepdream",
"## Background I... |
image-segmentation | keras |
## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook [to this Keras example on Multiclass semantic segmentation using DeepLabV3+](https://keras.io/examples/vision/deeplabv3_plus/).
Full credits to: [Soumik Rakshit](http://github.com/soumik12345)
The model is trained for ... | {"license": ["cc0-1.0"], "library_name": "keras", "tags": ["computer-vision", "image-segmentation"]} | keras-io/deeplabv3p-resnet50 | null | [
"keras",
"computer-vision",
"image-segmentation",
"arxiv:1811.12596",
"arxiv:1802.02611",
"arxiv:1706.05587",
"arxiv:1606.00915",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1811.12596",
"1802.02611",
"1706.05587",
"1606.00915"
] | [] | TAGS
#keras #computer-vision #image-segmentation #arxiv-1811.12596 #arxiv-1802.02611 #arxiv-1706.05587 #arxiv-1606.00915 #license-cc0-1.0 #has_space #region-us
|
## Multiclass semantic segmentation using DeepLabV3+
This repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.
Full credits to: Soumik Rakshit
The model is trained for demonstrative purposes and does not guarantee the best results in production. For bet... | [
"## Multiclass semantic segmentation using DeepLabV3+\nThis repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.\n\nFull credits to: Soumik Rakshit\n\nThe model is trained for demonstrative purposes and does not guarantee the best results in production... | [
"TAGS\n#keras #computer-vision #image-segmentation #arxiv-1811.12596 #arxiv-1802.02611 #arxiv-1706.05587 #arxiv-1606.00915 #license-cc0-1.0 #has_space #region-us \n",
"## Multiclass semantic segmentation using DeepLabV3+\nThis repo contains the model and the notebook to this Keras example on Multiclass semantic s... |
null | keras |
## Keras Implementation of Graph Attention Networks for Node Classification 🕸
This repo contains the model and the notebook [to this Keras example on Graph Attention Networks for Node Classification](https://keras.io/examples/graph/gat_node_classification/).
Full credits to: [Alexander Kensert](https://github.com/a... | {"license": ["cc0-1.0"], "tags": ["graph neural networks"], "thumbnail": "url to a thumbnail used in social sharing"} | keras-io/graph-attention-nets | null | [
"keras",
"graph neural networks",
"arxiv:1710.10903",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1710.10903"
] | [] | TAGS
#keras #graph neural networks #arxiv-1710.10903 #license-cc0-1.0 #region-us
|
## Keras Implementation of Graph Attention Networks for Node Classification
This repo contains the model and the notebook to this Keras example on Graph Attention Networks for Node Classification.
Full credits to: Alexander Kensert
## Background Information
Graph neural networks is the preferred neural network ar... | [
"## Keras Implementation of Graph Attention Networks for Node Classification \n\nThis repo contains the model and the notebook to this Keras example on Graph Attention Networks for Node Classification.\n\nFull credits to: Alexander Kensert",
"## Background Information \nGraph neural networks is the preferred neur... | [
"TAGS\n#keras #graph neural networks #arxiv-1710.10903 #license-cc0-1.0 #region-us \n",
"## Keras Implementation of Graph Attention Networks for Node Classification \n\nThis repo contains the model and the notebook to this Keras example on Graph Attention Networks for Node Classification.\n\nFull credits to: Alex... |
image-to-text | generic | ## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network. 🌃🌅🎑
This repo contains the models and the notebook [on Image captioning with visual attention](https://www.tensorflow.org/tutorials/text/image_captioning?hl=en).
Full credits to TensorFlow Team
## Background Information
... | {"license": "cc0-1.0", "library_name": "generic", "tags": ["image-to-text", "generic"], "pipeline_tag": "image-to-text", "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg", "example_title": "Kedis"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main... | keras-io/image-captioning | null | [
"generic",
"keras",
"image-to-text",
"arxiv:1502.03044",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1502.03044"
] | [] | TAGS
#generic #keras #image-to-text #arxiv-1502.03044 #license-cc0-1.0 #has_space #region-us
| ## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network.
This repo contains the models and the notebook on Image captioning with visual attention.
Full credits to TensorFlow Team
## Background Information
This notebook implements TensorFlow Keras implementation on Image captioni... | [
"## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network. \n\nThis repo contains the models and the notebook on Image captioning with visual attention.\n\nFull credits to TensorFlow Team",
"## Background Information\nThis notebook implements TensorFlow Keras implementation on ... | [
"TAGS\n#generic #keras #image-to-text #arxiv-1502.03044 #license-cc0-1.0 #has_space #region-us \n",
"## Tensorflow Keras Implementation of an Image Captioning Model with encoder-decoder network. \n\nThis repo contains the models and the notebook on Image captioning with visual attention.\n\nFull credits to Tensor... |
null | keras |
[Paper](https://arxiv.org/abs/2103.06255) | [Keras Tutorial](https://keras.io/examples/vision/involution/)
Author: [Aritra Roy Gosthipaty](https://twitter.com/ariG23498)
## Convolution Kernel
## Involution Kernel
 | {"license": "mit", "datasets": ["CIFAR10"]} | keras-io/involution | null | [
"keras",
"dataset:CIFAR10",
"arxiv:2103.06255",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.06255"
] | [] | TAGS
#keras #dataset-CIFAR10 #arxiv-2103.06255 #license-mit #has_space #region-us
|
Paper | Keras Tutorial
Author: Aritra Roy Gosthipaty
## Convolution Kernel
!conv
## Involution Kernel
!inv | [
"## Convolution Kernel\r\n!conv",
"## Involution Kernel\r\n!inv"
] | [
"TAGS\n#keras #dataset-CIFAR10 #arxiv-2103.06255 #license-mit #has_space #region-us \n",
"## Convolution Kernel\r\n!conv",
"## Involution Kernel\r\n!inv"
] |
image-to-image | keras |
## Zero-DCE for low-light image enhancement
**Original Author**: [Soumik Rakshit](https://github.com/soumik12345) <br>
**Date created**: 2021/09/18 <br>
**HF Contribution**: [Harveen Singh Chadha](https://github.com/harveenchadha)<br>
**Dataset**: [LOL Dataset](https://huggingface.co/Harveenchadha/low-light-image-e... | {"license": "apache-2.0", "library_name": "keras", "tags": ["image-to-image"]} | keras-io/low-light-image-enhancement | null | [
"keras",
"image-to-image",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-to-image #license-apache-2.0 #has_space #region-us
|
## Zero-DCE for low-light image enhancement
Original Author: Soumik Rakshit <br>
Date created: 2021/09/18 <br>
HF Contribution: Harveen Singh Chadha<br>
Dataset: LOL Dataset
## Spaces Demo
## Description: Implementing Zero-Reference Deep Curve Estimation for low-light image enhancement.
Zero-Reference Deep Curv... | [
"## Zero-DCE for low-light image enhancement\n\n\nOriginal Author: Soumik Rakshit <br>\nDate created: 2021/09/18 <br>\nHF Contribution: Harveen Singh Chadha<br>\nDataset: LOL Dataset",
"## Spaces Demo",
"## Description: Implementing Zero-Reference Deep Curve Estimation for low-light image enhancement.\n\n\nZero... | [
"TAGS\n#keras #image-to-image #license-apache-2.0 #has_space #region-us \n",
"## Zero-DCE for low-light image enhancement\n\n\nOriginal Author: Soumik Rakshit <br>\nDate created: 2021/09/18 <br>\nHF Contribution: Harveen Singh Chadha<br>\nDataset: LOL Dataset",
"## Spaces Demo",
"## Description: Implementing ... |
image-to-image | keras | ## Model description
This repo contains the model and the notebook [Low-light image enhancement using MIRNet](https://keras.io/examples/vision/mirnet/).
Full credits go to [Soumik Rakshit](https://github.com/soumik12345)
Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) with a slight change on hyp... | {"library_name": "keras", "tags": ["image-to-image"]} | keras-io/lowlight-enhance-mirnet | null | [
"keras",
"tensorboard",
"image-to-image",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tensorboard #image-to-image #has_space #region-us
| ## Model description
This repo contains the model and the notebook Low-light image enhancement using MIRNet.
Full credits go to Soumik Rakshit
Reproduced by Vu Minh Chien with a slight change on hyperparameters.
With the goal of recovering high-quality image content from its degraded version, image restoration enjoy... | [
"## Model description\nThis repo contains the model and the notebook Low-light image enhancement using MIRNet.\n\nFull credits go to Soumik Rakshit\n\nReproduced by Vu Minh Chien with a slight change on hyperparameters.\n\nWith the goal of recovering high-quality image content from its degraded version, image resto... | [
"TAGS\n#keras #tensorboard #image-to-image #has_space #region-us \n",
"## Model description\nThis repo contains the model and the notebook Low-light image enhancement using MIRNet.\n\nFull credits go to Soumik Rakshit\n\nReproduced by Vu Minh Chien with a slight change on hyperparameters.\n\nWith the goal of reco... |
image-classification | keras |
## Image Classification using MobileViT
This repo contains the model and the notebook [to this Keras example on MobileViT](https://keras.io/examples/vision/mobilevit/).
Full credits to: [Sayak Paul](https://twitter.com/RisingSayak)
## Background Information
MobileViT architecture (Mehta et al.), combines the benefi... | {"license": ["cc0-1.0"], "library_name": "keras", "tags": ["computer-vision", "image-classification"]} | keras-io/mobile-vit-xxs | null | [
"keras",
"computer-vision",
"image-classification",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #computer-vision #image-classification #license-cc0-1.0 #has_space #region-us
|
## Image Classification using MobileViT
This repo contains the model and the notebook to this Keras example on MobileViT.
Full credits to: Sayak Paul
## Background Information
MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can ca... | [
"## Image Classification using MobileViT\nThis repo contains the model and the notebook to this Keras example on MobileViT.\n\nFull credits to: Sayak Paul",
"## Background Information \nMobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformer... | [
"TAGS\n#keras #computer-vision #image-classification #license-cc0-1.0 #has_space #region-us \n",
"## Image Classification using MobileViT\nThis repo contains the model and the notebook to this Keras example on MobileViT.\n\nFull credits to: Sayak Paul",
"## Background Information \nMobileViT architecture (Mehta... |
image-segmentation | keras | ## Model description
The original idea from Keras examples [Monocular depth estimation](https://keras.io/examples/vision/depth_estimation/) of author [Victor Basu](https://www.linkedin.com/in/victor-basu-520958147/)
Full credits go to [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)
Depth estimation is a cruci... | {"library_name": "keras", "tags": ["image-segmentation"]} | keras-io/monocular-depth-estimation | null | [
"keras",
"tensorboard",
"image-segmentation",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tensorboard #image-segmentation #has_space #region-us
| Model description
-----------------
The original idea from Keras examples Monocular depth estimation of author Victor Basu
Full credits go to Vu Minh Chien
Depth estimation is a crucial step towards inferring scene geometry from 2D images. The goal in monocular depth estimation is to predict the depth value of ea... | [
"### Training hyperparameters\n\n\nModel architecture:\n\n\n* UNet with a pretrained DenseNet 201 backbone.\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-04\n* train\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_schedule... | [
"TAGS\n#keras #tensorboard #image-segmentation #has_space #region-us \n",
"### Training hyperparameters\n\n\nModel architecture:\n\n\n* UNet with a pretrained DenseNet 201 backbone.\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-04\n* train\\_batch\\_size: 16\n* seed: 42\... |
null | keras | ## Tensorflow Keras Implementation of Multimodal entailment.
This repo contains the models [Multimodal Entailment](https://keras.io/examples/nlp/multimodal_entailment/#dataset-visualization).
Credits: [Sayak Paul](https://twitter.com/RisingSayak) - Original Author
HF Contribution: [Rishav Chandra Varma](https://hugg... | {"tags": ["multimodal-entailment", "generic"]} | keras-io/multimodal-entailment | null | [
"keras",
"multimodal-entailment",
"generic",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #multimodal-entailment #generic #has_space #region-us
| ## Tensorflow Keras Implementation of Multimodal entailment.
This repo contains the models Multimodal Entailment.
Credits: Sayak Paul - Original Author
HF Contribution: Rishav Chandra Varma
## Background Information
### Introduction
In this example, we will build and train a model for predicting multimodal enta... | [
"## Tensorflow Keras Implementation of Multimodal entailment.\n\nThis repo contains the models Multimodal Entailment.\n\nCredits: Sayak Paul - Original Author\n\nHF Contribution: Rishav Chandra Varma",
"## Background Information",
"### Introduction\n\nIn this example, we will build and train a model for predict... | [
"TAGS\n#keras #multimodal-entailment #generic #has_space #region-us \n",
"## Tensorflow Keras Implementation of Multimodal entailment.\n\nThis repo contains the models Multimodal Entailment.\n\nCredits: Sayak Paul - Original Author\n\nHF Contribution: Rishav Chandra Varma",
"## Background Information",
"### I... |
null | keras | ## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.
This repo contains code using the model. [Named Entity Recognition using Transformers](https://keras.io/examples/nlp/ner_transformers/).
Credits: [Varun Singh](https://www.linkedin.com/in/varunsingh2/) - Original Author
HF Contributio... | {"tags": ["multimodal-entailment", "generic"]} | keras-io/ner-with-transformers | null | [
"keras",
"multimodal-entailment",
"generic",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #multimodal-entailment #generic #has_space #region-us
| ## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.
This repo contains code using the model. Named Entity Recognition using Transformers.
Credits: Varun Singh - Original Author
HF Contribution: Rishav Chandra Varma
## Background Information
### Introduction
Named Entity Recognitio... | [
"## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.\n\nThis repo contains code using the model. Named Entity Recognition using Transformers.\n\nCredits: Varun Singh - Original Author\n\nHF Contribution: Rishav Chandra Varma",
"## Background Information",
"### Introduction\n\nName... | [
"TAGS\n#keras #multimodal-entailment #generic #has_space #region-us \n",
"## Tensorflow Keras Implementation of Named Entity Recognition using Transformers.\n\nThis repo contains code using the model. Named Entity Recognition using Transformers.\n\nCredits: Varun Singh - Original Author\n\nHF Contribution: Rishav... |
image-to-text | keras |
## Keras Implementation of OCR model for reading captcha 🤖🦹🏻
This repo contains the model and the notebook [to this Keras example on OCR model for reading captcha](https://keras.io/examples/vision/captcha_ocr/).
Full credits to: [Aakash Kumar Nain](https://twitter.com/A_K_Nain)
## Background Information
This ex... | {"license": ["cc0-1.0"], "tags": ["ocr", "computer vision", "object detection", "image-to-text"]} | keras-io/ocr-for-captcha | null | [
"keras",
"ocr",
"computer vision",
"object detection",
"image-to-text",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #ocr #computer vision #object detection #image-to-text #license-cc0-1.0 #has_space #region-us
|
## Keras Implementation of OCR model for reading captcha
This repo contains the model and the notebook to this Keras example on OCR model for reading captcha.
Full credits to: Aakash Kumar Nain
## Background Information
This example demonstrates a simple OCR model built with the Functional API. Apart from combini... | [
"## Keras Implementation of OCR model for reading captcha \n\nThis repo contains the model and the notebook to this Keras example on OCR model for reading captcha.\n\nFull credits to: Aakash Kumar Nain",
"## Background Information \nThis example demonstrates a simple OCR model built with the Functional API. Apart... | [
"TAGS\n#keras #ocr #computer vision #object detection #image-to-text #license-cc0-1.0 #has_space #region-us \n",
"## Keras Implementation of OCR model for reading captcha \n\nThis repo contains the model and the notebook to this Keras example on OCR model for reading captcha.\n\nFull credits to: Aakash Kumar Nain... |
null | keras |
## Keras Implementation of PixelCNN on MNIST 🔢
This repo contains the model [PixelCNN](https://keras.io/examples/generative/pixelcnn/).
Sample images generated:
<img src="https://i.ibb.co/RDWbJBM/image.png" width="120" height='120'> <img src="https://i.ibb.co/kGPTDDb/104c083f-68e4-4d10-8b37-a242a7f10dd6.png" width... | {"license": ["cc0-1.0"], "tags": ["convnet", "mnist", "generative"]} | keras-io/pixel-cnn-mnist | null | [
"keras",
"convnet",
"mnist",
"generative",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #convnet #mnist #generative #license-cc0-1.0 #has_space #region-us
|
## Keras Implementation of PixelCNN on MNIST
This repo contains the model PixelCNN.
Sample images generated:
<img src="https://i.URL width="120" height='120'> <img src="https://i.URL width="120" height='120'> <img src="https://i.URL width="120" height='120'> <img src="https://i.URL width="120" height='120'>
Full... | [
"## Keras Implementation of PixelCNN on MNIST \n\nThis repo contains the model PixelCNN.\n\nSample images generated:\n\n<img src=\"https://i.URL width=\"120\" height='120'> <img src=\"https://i.URL width=\"120\" height='120'> <img src=\"https://i.URL width=\"120\" height='120'> <img src=\"https://i.URL width=\"120\... | [
"TAGS\n#keras #convnet #mnist #generative #license-cc0-1.0 #has_space #region-us \n",
"## Keras Implementation of PixelCNN on MNIST \n\nThis repo contains the model PixelCNN.\n\nSample images generated:\n\n<img src=\"https://i.URL width=\"120\" height='120'> <img src=\"https://i.URL width=\"120\" height='120'> <i... |
null | keras | ## Point cloud segmentation with PointNet
This repo contains [an Implementation of a PointNet-based model for segmenting point clouds.](https://keras.io/examples/vision/pointnet_segmentation/).
Full credits to [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://github.com/sayakpaul)
## Background... | {"license": "cc0-1.0", "tags": ["pointnet", "segmentation", "3d", "image"]} | keras-io/pointnet_segmentation | null | [
"keras",
"pointnet",
"segmentation",
"3d",
"image",
"arxiv:1612.00593",
"arxiv:1506.02025",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1612.00593",
"1506.02025"
] | [] | TAGS
#keras #pointnet #segmentation #3d #image #arxiv-1612.00593 #arxiv-1506.02025 #license-cc0-1.0 #has_space #region-us
| ## Point cloud segmentation with PointNet
This repo contains an Implementation of a PointNet-based model for segmenting point clouds..
Full credits to Soumik Rakshit, Sayak Paul
## Background Information
A "point cloud" is an important type of data structure for storing geometric shape data. Due to its irregular fo... | [
"## Point cloud segmentation with PointNet \n\nThis repo contains an Implementation of a PointNet-based model for segmenting point clouds..\n\nFull credits to Soumik Rakshit, Sayak Paul",
"## Background Information\nA \"point cloud\" is an important type of data structure for storing geometric shape data. Due to ... | [
"TAGS\n#keras #pointnet #segmentation #3d #image #arxiv-1612.00593 #arxiv-1506.02025 #license-cc0-1.0 #has_space #region-us \n",
"## Point cloud segmentation with PointNet \n\nThis repo contains an Implementation of a PointNet-based model for segmenting point clouds..\n\nFull credits to Soumik Rakshit, Sayak Paul... |
null | keras |
## Keras Implementation of Proximal Policy Optimization on Cartpole Environment 🔨🤖
This repo contains the model and the notebook [to this Keras example on PPO for Cartpole](https://keras.io/examples/rl/ppo_cartpole/).
Full credits to: Ilias Chrysovergis
## Backg... | {"license": ["cc0-1.0"], "tags": ["reinforcement learning", "proximal policy optimization"]} | keras-io/ppo-cartpole | null | [
"keras",
"reinforcement learning",
"proximal policy optimization",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #reinforcement learning #proximal policy optimization #license-cc0-1.0 #region-us
|
## Keras Implementation of Proximal Policy Optimization on Cartpole Environment
This repo contains the model and the notebook to this Keras example on PPO for Cartpole.
Full credits to: Ilias Chrysovergis
!cartpole_gif
## Background Information
### CartPole-v0
A pole is attached by an un-actuated joint to a ca... | [
"## Keras Implementation of Proximal Policy Optimization on Cartpole Environment \n\nThis repo contains the model and the notebook to this Keras example on PPO for Cartpole.\n\nFull credits to: Ilias Chrysovergis \n\n!cartpole_gif",
"## Background Information",
"### CartPole-v0\nA pole is attached by an un-act... | [
"TAGS\n#keras #reinforcement learning #proximal policy optimization #license-cc0-1.0 #region-us \n",
"## Keras Implementation of Proximal Policy Optimization on Cartpole Environment \n\nThis repo contains the model and the notebook to this Keras example on PPO for Cartpole.\n\nFull credits to: Ilias Chrysovergis... |
null | keras |
## RandAugment for Image Classification for Improved Robustness on the 🤗Hub!
[Paper](https://arxiv.org/abs/1909.13719) | [Keras Tutorial](https://keras.io/examples/vision/randaugment/)
Keras Tutorial Credit goes to : [Sayak Paul](https://twitter.com/RisingSayak)
**Excerpt from the Tutorial:**
Data augmentation is... | {"license": "apache-2.0", "tags": ["RandAugment", "Image Classification"], "datasets": ["cifar10"], "metrics": ["Accuracy"]} | keras-io/randaugment | null | [
"keras",
"RandAugment",
"Image Classification",
"dataset:cifar10",
"arxiv:1909.13719",
"arxiv:1911.04252",
"arxiv:1904.12848",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.13719",
"1911.04252",
"1904.12848"
] | [] | TAGS
#keras #RandAugment #Image Classification #dataset-cifar10 #arxiv-1909.13719 #arxiv-1911.04252 #arxiv-1904.12848 #license-apache-2.0 #has_space #region-us
|
## RandAugment for Image Classification for Improved Robustness on the Hub!
Paper | Keras Tutorial
Keras Tutorial Credit goes to : Sayak Paul
Excerpt from the Tutorial:
Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAug... | [
"## RandAugment for Image Classification for Improved Robustness on the Hub!\n\nPaper | Keras Tutorial\n\nKeras Tutorial Credit goes to : Sayak Paul\n\nExcerpt from the Tutorial:\n\nData augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (C... | [
"TAGS\n#keras #RandAugment #Image Classification #dataset-cifar10 #arxiv-1909.13719 #arxiv-1911.04252 #arxiv-1904.12848 #license-apache-2.0 #has_space #region-us \n",
"## RandAugment for Image Classification for Improved Robustness on the Hub!\n\nPaper | Keras Tutorial\n\nKeras Tutorial Credit goes to : Sayak Pau... |
image-segmentation | generic | ## Keras semantic segmentation models on the 🤗Hub! 🐶 🐕 🐩
Full credits go to [François Chollet](https://twitter.com/fchollet).
This repository contains the model from [this notebook on segmenting pets using U-net-like architecture](https://keras.io/examples/vision/oxford_pets_image_segmentation/). We've changed th... | {"license": "cc0-1.0", "library_name": "generic", "tags": ["image-segmentation", "generic"], "dataset": ["oxfort-iit pets"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg", "example_title": "Kedis"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/... | keras-io/semantic-segmentation | null | [
"generic",
"tf",
"image-segmentation",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#generic #tf #image-segmentation #license-cc0-1.0 #has_space #region-us
| ## Keras semantic segmentation models on the Hub!
Full credits go to François Chollet.
This repository contains the model from this notebook on segmenting pets using U-net-like architecture. We've changed the inference part to enable segmentation widget on the Hub. (see )
## Background Information
Image classif... | [
"## Keras semantic segmentation models on the Hub! \nFull credits go to François Chollet.\n\nThis repository contains the model from this notebook on segmenting pets using U-net-like architecture. We've changed the inference part to enable segmentation widget on the Hub. (see )",
"## Background Information \n\... | [
"TAGS\n#generic #tf #image-segmentation #license-cc0-1.0 #has_space #region-us \n",
"## Keras semantic segmentation models on the Hub! \nFull credits go to François Chollet.\n\nThis repository contains the model from this notebook on segmenting pets using U-net-like architecture. We've changed the inference pa... |
image-classification | keras | # Semi-supervised image classification using contrastive pretraining with SimCLR
## Description
This is a simple image classification model trained with **Semi-supervised image classification using contrastive pretraining with SimCLR**
The training procedure was done as seen in the example on <a href='https://keras.i... | {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification"], "datasets": ["STL-10"]} | keras-io/semi-supervised-classification-simclr | null | [
"keras",
"image-classification",
"dataset:STL-10",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-classification #dataset-STL-10 #license-apache-2.0 #has_space #region-us
| # Semi-supervised image classification using contrastive pretraining with SimCLR
## Description
This is a simple image classification model trained with Semi-supervised image classification using contrastive pretraining with SimCLR
The training procedure was done as seen in the example on <a href='URL target='_blank'... | [
"# Semi-supervised image classification using contrastive pretraining with SimCLR",
"## Description\n\nThis is a simple image classification model trained with Semi-supervised image classification using contrastive pretraining with SimCLR\nThe training procedure was done as seen in the example on <a href='URL tar... | [
"TAGS\n#keras #image-classification #dataset-STL-10 #license-apache-2.0 #has_space #region-us \n",
"# Semi-supervised image classification using contrastive pretraining with SimCLR",
"## Description\n\nThis is a simple image classification model trained with Semi-supervised image classification using contrastiv... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# keras-io/sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "keras-io/sentiment-analysis", "results": []}]} | keras-io/sentiment-analysis | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| keras-io/sentiment-analysis
===========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6865
* Validation Loss: 0.7002
* Train Accuracy: 0.4908
* Epoch: 4
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-04, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
null | keras |
## Keras Implementation of Convolutional Neural Networks for MNIST 1️⃣2️⃣3️⃣
This repo contains the model and the notebook [on Simple MNIST convnet](https://keras.io/examples/vision/mnist_convnet/).
Full credits to: [François Chollet](https://github.com/fchollet)
| {"license": ["cc0-1.0"], "tags": ["lstm"]} | keras-io/simple-mnist-convnet | null | [
"keras",
"lstm",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #lstm #license-cc0-1.0 #region-us
|
## Keras Implementation of Convolutional Neural Networks for MNIST 1️⃣2️⃣3️⃣
This repo contains the model and the notebook on Simple MNIST convnet.
Full credits to: François Chollet
| [
"## Keras Implementation of Convolutional Neural Networks for MNIST 1️⃣2️⃣3️⃣\nThis repo contains the model and the notebook on Simple MNIST convnet.\n\nFull credits to: François Chollet"
] | [
"TAGS\n#keras #lstm #license-cc0-1.0 #region-us \n",
"## Keras Implementation of Convolutional Neural Networks for MNIST 1️⃣2️⃣3️⃣\nThis repo contains the model and the notebook on Simple MNIST convnet.\n\nFull credits to: François Chollet"
] |
image-to-image | keras |
## Notes
* This model is a trained version of the Keras Tutorial [Image Super Resolution](https://keras.io/examples/vision/super_resolution_sub_pixel/)
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.
[Link to a pyimagesearch](https://www.pyimagesearch.com/2021/09/27/pixel-... | {"license": "mit", "tags": ["image-to-image"]} | keras-io/super-resolution | null | [
"keras",
"image-to-image",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #image-to-image #license-mit #has_space #region-us
|
## Notes
* This model is a trained version of the Keras Tutorial Image Super Resolution
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.
Link to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net. | [
"## Notes\n* This model is a trained version of the Keras Tutorial Image Super Resolution \n* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.\n\n\nLink to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net."
] | [
"TAGS\n#keras #image-to-image #license-mit #has_space #region-us \n",
"## Notes\n* This model is a trained version of the Keras Tutorial Image Super Resolution \n* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.\n\n\nLink to a pyimagesearch tutorial I worked on, where we h... |
image-classification | keras | A classification model trained with <a href='https://arxiv.org/abs/2004.11362' target='_blank'>**Supervised Contrastive Learning**</a> (Prannay Khosla et al.).
The training procedure was done as seen in the example on <a href='https://keras.io/examples/vision/supervised-contrastive-learning/' target='_blank'>**keras.io... | {"license": "apache-2.0", "library_name": "keras", "tags": ["image-classification"], "datasets": ["cifar10"]} | keras-io/supervised-contrastive-learning-cifar10 | null | [
"keras",
"image-classification",
"dataset:cifar10",
"arxiv:2004.11362",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.11362"
] | [] | TAGS
#keras #image-classification #dataset-cifar10 #arxiv-2004.11362 #license-apache-2.0 #has_space #region-us
| A classification model trained with <a href='URL target='_blank'>Supervised Contrastive Learning</a> (Prannay Khosla et al.).
The training procedure was done as seen in the example on <a href='URL target='_blank'>URL</a> by Khalid Salama.
The model was trained on cifar10, which includes ten classes: airplane, automob... | [] | [
"TAGS\n#keras #image-classification #dataset-cifar10 #arxiv-2004.11362 #license-apache-2.0 #has_space #region-us \n"
] |
image-classification | keras | ## Image classification with Swin Transformers on the 🤗Hub!
Author: [Kelvin Idanwekhai](https://twitter.com/KelvinIdan).
[Paper](https://arxiv.org/abs/2103.14030) | [Keras Tutorial](https://keras.io/examples/vision/swin_transformers/)
Excerpt from the Tutorial:
Swin Transformer (Shifted Window Transformer) can se... | {"license": "cc0-1.0", "library_name": "keras", "tags": ["swin-transformers", "Keras", "image-classification"], "dataset": ["CIFAR-100"]} | keras-io/swin-transformers | null | [
"keras",
"swin-transformers",
"Keras",
"image-classification",
"arxiv:2103.14030",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.14030"
] | [] | TAGS
#keras #swin-transformers #Keras #image-classification #arxiv-2103.14030 #license-cc0-1.0 #has_space #region-us
| ## Image classification with Swin Transformers on the Hub!
Author: Kelvin Idanwekhai.
Paper | Keras Tutorial
Excerpt from the Tutorial:
Swin Transformer (Shifted Window Transformer) can serve as a general-purpose backbone for computer vision. Swin Transformer is a hierarchical Transformer whose representations are... | [
"## Image classification with Swin Transformers on the Hub! \n\nAuthor: Kelvin Idanwekhai.\n\nPaper | Keras Tutorial\n\nExcerpt from the Tutorial:\n\nSwin Transformer (Shifted Window Transformer) can serve as a general-purpose backbone for computer vision. Swin Transformer is a hierarchical Transformer whose repres... | [
"TAGS\n#keras #swin-transformers #Keras #image-classification #arxiv-2103.14030 #license-cc0-1.0 #has_space #region-us \n",
"## Image classification with Swin Transformers on the Hub! \n\nAuthor: Kelvin Idanwekhai.\n\nPaper | Keras Tutorial\n\nExcerpt from the Tutorial:\n\nSwin Transformer (Shifted Window Transfo... |
text-generation | keras |
## Keras Implementation of Text generation with a miniature GPT
This repo contains the model and the notebook [to this Keras example on Text generation with a miniature GPT](https://keras.io/examples/generative/text_generation_with_miniature_gpt/).
Full credits to: [fchollet](https://twitter.com/fchollet)
## Backg... | {"language": "en", "license": "gpl", "tags": ["gpt", "text-generation"], "widget": [{"text": "Once upon a time, "}]} | keras-io/text-generation-miniature-gpt | null | [
"keras",
"gpt2",
"gpt",
"text-generation",
"en",
"license:gpl",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#keras #gpt2 #gpt #text-generation #en #license-gpl #has_space #region-us
|
## Keras Implementation of Text generation with a miniature GPT
This repo contains the model and the notebook to this Keras example on Text generation with a miniature GPT.
Full credits to: fchollet
## Background Information
This example demonstrates how to implement text generation with a miniature GPT model. Th... | [
"## Keras Implementation of Text generation with a miniature GPT\n\nThis repo contains the model and the notebook to this Keras example on Text generation with a miniature GPT.\n\nFull credits to: fchollet",
"## Background Information \nThis example demonstrates how to implement text generation with a miniature G... | [
"TAGS\n#keras #gpt2 #gpt #text-generation #en #license-gpl #has_space #region-us \n",
"## Keras Implementation of Text generation with a miniature GPT\n\nThis repo contains the model and the notebook to this Keras example on Text generation with a miniature GPT.\n\nFull credits to: fchollet",
"## Background Inf... |
null | keras |
## Keras Implementation of time series anomaly detection using an Autoencoder ⌛
This repo contains the model and the notebook [for this time series anomaly detection implementation of Keras](https://keras.io/examples/timeseries/timeseries_anomaly_detection/).
Full credits to: [Pavithra Vijay](https://github.com/pavi... | {"license": ["cc0-1.0"], "tags": ["autoencoder", "time series", "anomaly detection"]} | keras-io/time-series-anomaly-detection-autoencoder | null | [
"keras",
"autoencoder",
"time series",
"anomaly detection",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #autoencoder #time series #anomaly detection #license-cc0-1.0 #region-us
|
## Keras Implementation of time series anomaly detection using an Autoencoder ⌛
This repo contains the model and the notebook for this time series anomaly detection implementation of Keras.
Full credits to: Pavithra Vijay
## Background Information
This notebook demonstrates how you can use a reconstruction convolut... | [
"## Keras Implementation of time series anomaly detection using an Autoencoder ⌛\n\nThis repo contains the model and the notebook for this time series anomaly detection implementation of Keras.\n\nFull credits to: Pavithra Vijay",
"## Background Information\nThis notebook demonstrates how you can use a reconstruc... | [
"TAGS\n#keras #autoencoder #time series #anomaly detection #license-cc0-1.0 #region-us \n",
"## Keras Implementation of time series anomaly detection using an Autoencoder ⌛\n\nThis repo contains the model and the notebook for this time series anomaly detection implementation of Keras.\n\nFull credits to: Pavithra... |
null | keras | ## Timeseries classification with a Transformer model on the 🤗Hub!
Full credits go to [Theodoros Ntakouris](https://github.com/ntakouris).
This repository contains the model from [this notebook on time-series classification using the attention mechanism](https://keras.io/examples/timeseries/timeseries_classification... | {"license": "cc0-1.0", "library_name": "keras", "tags": ["time-series"], "dataset": ["FordA"]} | keras-io/timeseries_transformer_classification | null | [
"keras",
"time-series",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #time-series #license-cc0-1.0 #has_space #region-us
| ## Timeseries classification with a Transformer model on the Hub!
Full credits go to Theodoros Ntakouris.
This repository contains the model from this notebook on time-series classification using the attention mechanism.
The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset... | [
"## Timeseries classification with a Transformer model on the Hub! \nFull credits go to Theodoros Ntakouris.\n\nThis repository contains the model from this notebook on time-series classification using the attention mechanism. \n\nThe dataset we are using here is called FordA. The data comes from the UCR archive. T... | [
"TAGS\n#keras #time-series #license-cc0-1.0 #has_space #region-us \n",
"## Timeseries classification with a Transformer model on the Hub! \nFull credits go to Theodoros Ntakouris.\n\nThis repository contains the model from this notebook on time-series classification using the attention mechanism. \n\nThe dataset ... |
question-answering | transformers | <!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Question Answering with Hugging Face Transformers and Keras 🤗❤️
This model is a fine-tuned version of [distilbert-base-cased](https://... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["squad"], "metrics": ["f1"], "widget": [{"context": "Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user ac... | keras-io/transformers-qa | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
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"dataset:squad",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #dataset-squad #base_model-distilbert-base-cased #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Question Answering with Hugging Face Transformers and Keras ️
=============================================================
This model is a fine-tuned version of distilbert-base-cased on SQuAD dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9300
* Validation Loss: 1.1437
* Epoch: 1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### F... | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #dataset-squad #base_model-distilbert-base-cased #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer... |
video-classification | keras | # 🎬 Video Classification with a CNN-RNN Architecture
**Author:** Sayak Paul
**Date created:** 2021/05/28
**Last modified:** 2021/06/05
**Description:** Training a video classifier with transfer learning and a recurrent model on the UCF101 dataset.
**Keras documentation [link](https://keras.io/example... | {"library_name": "keras", "tags": ["computer-vision", "video-classification"]} | keras-io/video-classification-cnn-rnn | null | [
"keras",
"tensorboard",
"computer-vision",
"video-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tensorboard #computer-vision #video-classification #has_space #region-us
| # Video Classification with a CNN-RNN Architecture
Author: Sayak Paul
Date created: 2021/05/28
Last modified: 2021/06/05
Description: Training a video classifier with transfer learning and a recurrent model on the UCF101 dataset.
Keras documentation link
This example demonstrates video classificat... | [
"# Video Classification with a CNN-RNN Architecture\n\nAuthor: Sayak Paul \nDate created: 2021/05/28 \nLast modified: 2021/06/05 \nDescription: Training a video classifier with transfer learning and a recurrent model on the UCF101 dataset. \nKeras documentation link \n\nThis example demonstrates vide... | [
"TAGS\n#keras #tensorboard #computer-vision #video-classification #has_space #region-us \n",
"# Video Classification with a CNN-RNN Architecture\n\nAuthor: Sayak Paul \nDate created: 2021/05/28 \nLast modified: 2021/06/05 \nDescription: Training a video classifier with transfer learning and a recurrent ... |
null | keras |
## Keras Implementation of Video Vision Transformer on medmnist
This repo contains the model [to this Keras example on Video Vision Transformer](https://keras.io/examples/vision/vivit/).
## Background Information
This example implements [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Arnab ... | {"license": "apache-2.0", "library_name": "keras", "title": "Video Vision Transformer on medmnist", "emoji": "\ud83e\uddd1\u200d\u2695\ufe0f", "colorFrom": "red", "colorTo": "green", "sdk": "gradio", "app_file": "app.py", "pinned": false} | keras-io/video-vision-transformer | null | [
"keras",
"arxiv:2103.15691",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.15691"
] | [] | TAGS
#keras #arxiv-2103.15691 #license-apache-2.0 #has_space #region-us
|
## Keras Implementation of Video Vision Transformer on medmnist
This repo contains the model to this Keras example on Video Vision Transformer.
## Background Information
This example implements ViViT: A Video Vision Transformer by Arnab et al., a pure Transformer-based model for video classification. The authors pr... | [
"## Keras Implementation of Video Vision Transformer on medmnist\n\nThis repo contains the model to this Keras example on Video Vision Transformer.",
"## Background Information \nThis example implements ViViT: A Video Vision Transformer by Arnab et al., a pure Transformer-based model for video classification. The... | [
"TAGS\n#keras #arxiv-2103.15691 #license-apache-2.0 #has_space #region-us \n",
"## Keras Implementation of Video Vision Transformer on medmnist\n\nThis repo contains the model to this Keras example on Video Vision Transformer.",
"## Background Information \nThis example implements ViViT: A Video Vision Transfor... |
image-classification | keras | # Train a Vision Transformer on small datasets
Author: [Aritra Roy Gosthipaty](https://twitter.com/ariG23498)
[Keras Blog](https://keras.io/examples/vision/vit_small_ds/) | [Colab Notebook](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/vit_small_ds.ipynb)
In the acade... | {"license": "apache-2.0", "tags": ["image-classification", "keras"]} | keras-io/vit-small-ds | null | [
"keras",
"image-classification",
"arxiv:2010.11929",
"arxiv:2112.13492",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2112.13492"
] | [] | TAGS
#keras #image-classification #arxiv-2010.11929 #arxiv-2112.13492 #license-apache-2.0 #region-us
| # Train a Vision Transformer on small datasets
Author: Aritra Roy Gosthipaty
Keras Blog | Colab Notebook
In the academic paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, the authors mention that Vision Transformers (ViT) are data-hungry. Therefore, pretraining a ViT on a large-sized ... | [
"# Train a Vision Transformer on small datasets\n\nAuthor: Aritra Roy Gosthipaty\n\nKeras Blog | Colab Notebook\n\nIn the academic paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, the authors mention that Vision Transformers (ViT) are data-hungry. Therefore, pretraining a ViT on a l... | [
"TAGS\n#keras #image-classification #arxiv-2010.11929 #arxiv-2112.13492 #license-apache-2.0 #region-us \n",
"# Train a Vision Transformer on small datasets\n\nAuthor: Aritra Roy Gosthipaty\n\nKeras Blog | Colab Notebook\n\nIn the academic paper An Image is Worth 16x16 Words: Transformers for Image Recognition at ... |
image-classification | keras | # Train a Vision Transformer on small datasets
Author: [Jónathan Heras](https://twitter.com/_Jonathan_Heras)
[Keras Blog](https://keras.io/examples/vision/vit_small_ds/) | [Colab Notebook](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/vit_small_ds.ipynb)
In the academ... | {"license": "apache-2.0", "tags": ["image-classification", "keras"]} | keras-io/vit_small_ds_v2 | null | [
"keras",
"image-classification",
"arxiv:2010.11929",
"arxiv:2112.13492",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.11929",
"2112.13492"
] | [] | TAGS
#keras #image-classification #arxiv-2010.11929 #arxiv-2112.13492 #license-apache-2.0 #has_space #region-us
| # Train a Vision Transformer on small datasets
Author: Jónathan Heras
Keras Blog | Colab Notebook
In the academic paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, the authors mention that Vision Transformers (ViT) are data-hungry. Therefore, pretraining a ViT on a large-sized dataset... | [
"# Train a Vision Transformer on small datasets\n\nAuthor: Jónathan Heras\n\nKeras Blog | Colab Notebook\n\nIn the academic paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, the authors mention that Vision Transformers (ViT) are data-hungry. Therefore, pretraining a ViT on a large-si... | [
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"# Train a Vision Transformer on small datasets\n\nAuthor: Jónathan Heras\n\nKeras Blog | Colab Notebook\n\nIn the academic paper An Image is Worth 16x16 Words: Transformers for Image Recognition... |
fill-mask | transformers | ### Overview
This is a slightly smaller model trained on [OSCAR](https://oscar-corpus.com/) Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model Specification
... | {"language": "si", "tags": ["SinhalaBERTo", "Sinhala", "roberta"], "datasets": ["oscar"]} | keshan/SinhalaBERTo | null | [
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"dataset:oscar",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
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] | TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #SinhalaBERTo #Sinhala #si #dataset-oscar #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Overview
This is a slightly smaller model trained on OSCAR Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model Specification
The model chosen for training... | [
"### Overview\n\nThis is a slightly smaller model trained on OSCAR Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.",
"## Model Specification\n\n\nThe model chosen... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #SinhalaBERTo #Sinhala #si #dataset-oscar #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Overview\n\nThis is a slightly smaller model trained on OSCAR Sinhala dedup dataset. As Sinhala is one of ... |
text-generation | transformers | This is a finetunes version of keshan/sinhala-gpt2 with newswire articles. This was finetuned on ~12MB of data
- Num examples=8395
- Batch size =8
It got a Perplexity of 3.15 | {"language": "si", "tags": ["sinhala", "gpt2"], "pipeline_tag": "text-generation", "widget": [{"text": "\u0db8\u0db8"}]} | keshan/sinhala-gpt2-newswire | null | [
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"gpt2",
"text-generation",
"sinhala",
"si",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #sinhala #si #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This is a finetunes version of keshan/sinhala-gpt2 with newswire articles. This was finetuned on ~12MB of data
- Num examples=8395
- Batch size =8
It got a Perplexity of 3.15 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #sinhala #si #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers | ### Overview
This is a smaller GPT2 model trained on [MC4](https://github.com/allenai/allennlp/discussions/5056) Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model S... | {"language": "si", "tags": ["Sinhala", "text-generation", "gpt2"], "datasets": ["mc4"]} | keshan/sinhala-gpt2 | null | [
"transformers",
"pytorch",
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"jax",
"tensorboard",
"gpt2",
"feature-extraction",
"Sinhala",
"text-generation",
"si",
"dataset:mc4",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #text-generation-inference #region-us
| ### Overview
This is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model Specification
The model chosen for training is GPT2 wit... | [
"### Overview\n\nThis is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.",
"## Model Specification\n\n\nThe model chosen for trainin... | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #gpt2 #feature-extraction #Sinhala #text-generation #si #dataset-mc4 #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\nThis is a smaller GPT2 model trained on MC4 Sinhala dataset. As Sinhala is one of those low resource languages,... |
fill-mask | transformers | # Sinhala roberta on mc4 dataset
| {"language": "si", "license": "cc-by-4.0", "tags": ["sinhala", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "\u0db8\u0db8 \u0dc3\u0dd2\u0d82\u0dc4\u0dbd \u0db7\u0dcf\u0dc2\u0dcf\u0dc0 <mask>"}]} | keshan/sinhala-roberta-mc4 | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"sinhala",
"si",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"si"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #sinhala #si #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| # Sinhala roberta on mc4 dataset
| [
"# Sinhala roberta on mc4 dataset"
] | [
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"# Sinhala roberta on mc4 dataset"
] |
fill-mask | transformers | ### Overview
This is a slightly smaller model trained on [OSCAR](https://oscar-corpus.com/) Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model Specification
... | {"language": "si", "tags": ["oscar", "Sinhala", "roberta", "fill-mask"], "datasets": ["oscar"], "widget": [{"text": "\u0db8\u0db8 \u0dc3\u0dd2\u0d82\u0dc4\u0dbd \u0db7\u0dcf\u0dc2\u0dcf\u0dc0 <mask>"}]} | keshan/sinhala-roberta-oscar | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"oscar",
"Sinhala",
"si",
"dataset:oscar",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"si"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #oscar #Sinhala #si #dataset-oscar #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
| ### Overview
This is a slightly smaller model trained on OSCAR Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model Specification
The model chosen for training... | [
"### Overview\n\nThis is a slightly smaller model trained on OSCAR Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.",
"## Model Specification\n\n\nThe model chosen... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #oscar #Sinhala #si #dataset-oscar #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\nThis is a slightly smaller model trained on OSCAR Sinhala dedup dataset. As Sinhala is one of those low resource lan... |
null | transformers | # kevinrobinson/perturbations_table_quickstart model card
This is just for UI smoke testing, and shouldn't be used for anything else.
It's built from https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py.
| {} | kevinrobinson/perturbations_table_quickstart_sst | null | [
"transformers",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #endpoints_compatible #region-us
| # kevinrobinson/perturbations_table_quickstart model card
This is just for UI smoke testing, and shouldn't be used for anything else.
It's built from URL
| [
"# kevinrobinson/perturbations_table_quickstart model card\n\nThis is just for UI smoke testing, and shouldn't be used for anything else.\n\nIt's built from URL"
] | [
"TAGS\n#transformers #tf #bert #endpoints_compatible #region-us \n",
"# kevinrobinson/perturbations_table_quickstart model card\n\nThis is just for UI smoke testing, and shouldn't be used for anything else.\n\nIt's built from URL"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-bert-wwm-ext-finetuned-cola
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "chinese-bert-wwm-ext-finetuned-cola", "results": []}]} | kevinzyz/chinese-bert-wwm-ext-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-bert-wwm-ext-finetuned-cola
===================================
This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5747
* Matthews Correlation: 0.4085
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
image-to-text | transformers |
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to prov... | {"language": "ja", "license": "apache-2.0", "tags": ["image-to-text"], "datasets": ["manga109s"]} | kha-white/manga-ocr-base | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"ja",
"dataset:manga109s",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #ja #dataset-manga109s #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses Vision Encoder Decoder framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality
text recognition, robust against various scenarios specifi... | [
"# Manga OCR\n\nOptical character recognition for Japanese text, with the main focus being Japanese manga.\n\nIt uses Vision Encoder Decoder framework.\n\nManga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality\ntext recognition, robust against various scenar... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #image-to-text #ja #dataset-manga109s #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Manga OCR\n\nOptical character recognition for Japanese text, with the main focus being Japanese manga.\n\nIt uses Vision Encoder Decoder framework.\n... |
text-classification | transformers |
# DeBERTa-v3-large-mnli
## Model description
This model was trained on the Multi-Genre Natural Language Inference ( MultiNLI ) dataset, which consists of 433k sentence pairs textual entailment information.
The model used is [DeBERTa-v3-large from Microsoft](https://huggingface.co/microsoft/deberta-large). The v3 D... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "The Movie have been criticized for the story. However, I think it is a great movie. [SEP] I liked the movie."}]} | khalidalt/DeBERTa-v3-large-mnli | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #zero-shot-classification #en #arxiv-2006.03654 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# DeBERTa-v3-large-mnli
## Model description
This model was trained on the Multi-Genre Natural Language Inference ( MultiNLI ) dataset, which consists of 433k sentence pairs textual entailment information.
The model used is DeBERTa-v3-large from Microsoft. The v3 DeBERTa outperforms the result of Bert and RoBERTa ... | [
"# DeBERTa-v3-large-mnli",
"## Model description\n\nThis model was trained on the Multi-Genre Natural Language Inference ( MultiNLI ) dataset, which consists of 433k sentence pairs textual entailment information. \n\nThe model used is DeBERTa-v3-large from Microsoft. The v3 DeBERTa outperforms the result of Bert ... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #zero-shot-classification #en #arxiv-2006.03654 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# DeBERTa-v3-large-mnli",
"## Model description\n\nThis model was trained on the Multi-Genre Natural Language Inference ( MultiNL... |
text-generation | transformers |
<!--
---
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bengali-lyricist-gpt2
results:
- task:
name: Causal Language Modeling
type: text-generation
---
-->
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should pr... | {"language": "bn", "tags": ["text generation", "bengali", "gpt2", "bangla", "causal-lm"], "widget": [{"text": "\u099c\u09c0\u09ac\u09a8\u09c7\u09b0 \u09ae\u09be\u09a8\u09c7 "}], "pipeline_tag": "text-generation"} | khalidsaifullaah/bengali-lyricist-gpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"text generation",
"bengali",
"bangla",
"causal-lm",
"bn",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #text generation #bengali #bangla #causal-lm #bn #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| bengali-lyricist-gpt2
=====================
This model is a fine-tuned version of flax-community/gpt2-bengali on the Bengali Song Lyrics dataset from Kaggle.
It achieves the following results on the evaluation set:
* Loss: 2.1199
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #text generation #bengali #bangla #causal-lm #bn #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text2text-generation | transformers | # keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[![pypi Version](https://img.shields.io/pypi/v/ke... | {"language": "en", "license": "mit", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | khanglam7012/t5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # keytotext
!keytotext (1)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
## Model:
Keytotext is based on the Amazing T5 Model:
- 'k2t': Model
- 'k2t-... | [
"# keytotext\n!keytotext (1)\nIdea is to build a model which will take keywords as inputs and generate sentences as outputs.",
"### Keytotext is powered by Huggingface \n![pypi Version](URL\n![Downloads](URL\n![Open In Colab](URL\n![Streamlit App](URL",
"## Model:\nKeytotext is based on the Amazing T5 Model: \n... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #keytotext #k2t #Keywords to Sentences #en #dataset-WebNLG #dataset-Dart #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# keytotext\n!keytotext (1)\nIdea is to build a model which will take keywords as in... |
text2text-generation | transformers |
# IndoBART-v2 Model fine-tuned version
Fine-tuned version of IndoBART-v2 with machine translation id->su using default hyperparameter from indoBART paper.
by Ryan Abdurohman
# IndoBART-v2 Model
[IndoBART-v2](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the BART mod... | {"language": "id", "license": "mit", "tags": ["indogpt", "indobenchmark", "indonlg"], "datasets": ["Indo4B+"], "inference": false} | khavitidala/finetuned-indobartv2-id-su | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"indogpt",
"indobenchmark",
"indonlg",
"id",
"arxiv:2104.08200",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08200"
] | [
"id"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #region-us
| IndoBART-v2 Model fine-tuned version
====================================
Fine-tuned version of IndoBART-v2 with machine translation id->su using default hyperparameter from indoBART paper.
by Ryan Abdurohman
IndoBART-v2 Model
=================
IndoBART-v2 is a state-of-the-art language model for Indonesian bas... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #indogpt #indobenchmark #indonlg #id #arxiv-2104.08200 #license-mit #autotrain_compatible #region-us \n"
] |
text-classification | transformers | # Unreliable News Classifier (English)
Trained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.
This model used the pre-trained weights of `bert-base-cased` as starting point and was able to achieve 84% accur... | {} | khizon/bert-unreliable-news-eng | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Unreliable News Classifier (English)
Trained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.
This model used the pre-trained weights of 'bert-base-cased' as starting point and was able to achieve 84% accur... | [
"# Unreliable News Classifier (English)\nTrained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.\nThis model used the pre-trained weights of 'bert-base-cased' as starting point and was able to achieve 84... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Unreliable News Classifier (English)\nTrained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the... |
text-classification | transformers | # Unreliable News Classifier (English)
Trained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.
This model used the pre-trained weights of `distilbert-base-cased` as starting point (only 4 layers) and was abl... | {} | khizon/distilbert-unreliable-news-eng-4L | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Unreliable News Classifier (English)
Trained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.
This model used the pre-trained weights of 'distilbert-base-cased' as starting point (only 4 layers) and was abl... | [
"# Unreliable News Classifier (English)\nTrained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news sources between the three sets.\nThis model used the pre-trained weights of 'distilbert-base-cased' as starting point (only 4 layers) and ... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Unreliable News Classifier (English)\nTrained, validate, and tested using a subset of the NELA-GT-2018 dataset. The dataset is split such that there was no overlap in of news so... |
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-georgian2-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-georgian2-colab", "results": []}]} | kika2000/wav2vec2-large-xls-r-300m-kika10 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-georgian2-colab
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4317
* Wer: 0.4280
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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-kika4_my-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-kika4_my-colab", "results": []}]} | kika2000/wav2vec2-large-xls-r-300m-kika4_my-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-kika4_my-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# wav2vec2-large-xls-r-300m-kika4_my-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-kika4_my-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common... |
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-kika5_my-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-kika5_my-colab", "results": []}]} | kika2000/wav2vec2-large-xls-r-300m-kika5_my-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-kika5\_my-colab
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3860
* Wer: 0.3505
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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-kika_my-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-kika_my-colab", "results": []}]} | kika2000/wav2vec2-large-xls-r-300m-kika_my-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-kika\_my-colab
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3300
* Wer: 0.5804
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text-generation | transformers | # Source Code
[<img src="https://api.flatworld.co/wp-content/uploads/2020/10/DAGsHub-Logo.png" alt="dagshub" width="150"/>](https://dagshub.com/kingabzpro/DailoGPT-RickBot)
[](https://github.com/kingabzpr... | {"language": ["en"], "library_name": "transformers", "tags": ["gpt-2"], "datasets": ["ysharma/rickandmorty"], "metrics": ["perplexity"], "pipeline_tag": "conversational"} | kingabzpro/DialoGPT-small-Rick-Bot | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"gpt-2",
"conversational",
"en",
"dataset:ysharma/rickandmorty",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #gpt-2 #conversational #en #dataset-ysharma/rickandmorty #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Source Code
<img src="URL alt="dagshub" width="150"/>
![DAGsHub](URL
# Testing
Result
perplexity : 8.53 | [
"# Source Code\n<img src=\"URL alt=\"dagshub\" width=\"150\"/>\n\n![DAGsHub](URL",
"# Testing\n\n\n\nResult\n\n perplexity : 8.53"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #gpt-2 #conversational #en #dataset-ysharma/rickandmorty #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Source Code\n<img src=\"URL alt=\"dagshub\" width=\"150\"/>\n\n![DAGsHub](URL",
"# Testing\n\n\n\nR... |
translation | transformers | ## Predicting English Translation
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Loading tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("kingabzpro/Helsinki-NLP-opus-yor-mul-en")
model = AutoModelForSeq2SeqLM.from_pretrained("kingabzpro/Helsinki-NLP-opus-yor-mul-en").to('cuda'... | {"language": ["yo", "en"], "license": "apache-2.0", "tags": ["text", "machine-translation", "language-translation", "seq2seq", "helsinki-nlp"], "metrics": ["rouge"], "pipeline_tag": "translation"} | kingabzpro/Helsinki-NLP-opus-yor-mul-en | null | [
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"text",
"machine-translation",
"language-translation",
"seq2seq",
"helsinki-nlp",
"translation",
"yo",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"yo",
"en"
] | TAGS
#transformers #pytorch #safetensors #marian #text2text-generation #text #machine-translation #language-translation #seq2seq #helsinki-nlp #translation #yo #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ## Predicting English Translation
## Result
## ROGUE Score
0.3025 | [
"## Predicting English Translation",
"## Result",
"## ROGUE Score\n0.3025"
] | [
"TAGS\n#transformers #pytorch #safetensors #marian #text2text-generation #text #machine-translation #language-translation #seq2seq #helsinki-nlp #translation #yo #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Predicting English Translation",
"## Result",
"## ROGUE Scor... |
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. -->
# wav2vec2-60-Urdu-V8
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/H... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "Harveenchadha/vakyansh-wav2vec2-urdu-urm-60", "model-index": [{"name": "wav2vec2-urdu-V8-Abi... | kingabzpro/wav2vec2-60-Urdu-V8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"ur",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:Harveenchadha/vakyansh-wav2vec2-urdu-urm-60",
"license:apache-2.0",
"model-index",
"endpoints_co... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #ur #dataset-mozilla-foundation/common_voice_8_0 #base_model-Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-60-Urdu-V8
===================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 11.4832
* Wer: 0.5729
* Cer: 0.3170
### Training hyperparameters
The following hyperparameters ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\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 epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #ur #dataset-mozilla-foundation/common_voice_8_0 #base_model-Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training ... |
automatic-speech-recognition | transformers | # wav2vec2-large-xlsr-53-urdu
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Wer: 0.5913
- Cer: 0.3310
## Model description
T... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-60-urdu", "results": [{"task": {"type": "automatic-speech-recognition", ... | kingabzpro/wav2vec2-60-urdu | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ur",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ur #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-urdu
===========================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Wer: 0.5913
* Cer: 0.3310
Model description
-----------------
The training and valid ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ur #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpar... |
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-1b-Indonesian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/fac... | {"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-large-xls-r-1b-Indonesian", ... | kingabzpro/wav2vec2-large-xls-r-1b-Indonesian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"id",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-1b-Indonesian
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9550
* Wer: 0.4551
* Cer: 0.1643
### Training hyperparameters
The following hype... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe follo... |
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-1b-Irish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook... | {"language": ["ga"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-large-xls-r-1b-Irish-Abid", ... | kingabzpro/wav2vec2-large-xls-r-1b-Irish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"ga",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"regio... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #ga #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-large-xls-r-1b-Irish
=============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3599
* Wer: 0.4236
* Cer: 0.1768
#### Evaluation Commands
1. To evaluate on 'mozilla-found... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'",
"### Inference With LM",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #ga #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"#### Evaluation Commands\n\n\n1.... |
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-1b-Swedish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebo... | {"language": ["sv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-large-xls-r-1b-Swedish", "re... | kingabzpro/wav2vec2-large-xls-r-1b-Swedish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"sv",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-1b-Swedish
===============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
Without LM
* Loss: 0.3370
* Wer: 18.44
* Cer: 5.75
With LM
* Loss: 0.3370
* Wer: 14.04
* Cer: ... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'\n2. To evaluate on 'speech-recognition-community-v2/dev\\_data'",
"### Inference With LM",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"#### Evaluation Commands\n\n\n1. To evaluat... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-Indonesian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-Indonesian", "results": [{"task": {"type": "automatic-s... | kingabzpro/wav2vec2-large-xls-r-300m-Indonesian | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"id",
"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 | [] | [
"id"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-Indonesian
====================================
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: 0.4087
* Wer: 0.2461
* Cer: 0.0666
### Training hyperparameters
The followin... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were ... |
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-Swedish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"language": ["sv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-xls-r-300m-swedish", "resu... | kingabzpro/wav2vec2-large-xls-r-300m-Swedish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"sv",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-Swedish
=================================
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: 0.3641
* Wer: 0.2473
* Cer: 0.0758
Training procedure
------------------
### Train... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe fol... |
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-Tatar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"language": ["tt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-large-xls-r-300m-Tatar", "... | kingabzpro/wav2vec2-large-xls-r-300m-Tatar | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"tt",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #tt #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-Tatar
===============================
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: 0.5068
* Wer: 0.4263
* Cer: 0.1117
#### Evaluation Commands
1. To evaluate on 'mozilla... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'",
"### Inference With LM",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #tt #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"#### Evaluation Commands\n\n\n1. To evalu... |
automatic-speech-recognition | transformers | ---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-Urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"language": ["ur"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-large-xls-r-300m-Urdu", "results": [{"t... | kingabzpro/wav2vec2-large-xls-r-300m-Urdu | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ur",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ur #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
---
wav2vec2-large-xls-r-300m-Urdu
==============================
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: 0.9889
* Wer: 0.5607
* Cer: 0.2370
#### Evaluation Commands
1. To evaluate on 'm... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'",
"### Inference With LM",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size:... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ur #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-300-arabic
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceb... | {"language": ["ar"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-xls-r-300m-arabic", "resul... | kingabzpro/wav2vec2-large-xlsr-300-arabic | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ar",
"dataset:mozilla-foundation/common_voice_7_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"r... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xlsr-300-arabic
==============================
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: 0.4514
* Wer: 0.4256
* Cer: 0.1528
#### Evaluation Commands
1. To evaluate on 'mozilla-f... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_7\\_0' with split 'test'",
"### Inference With LM",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size:... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"#### Evaluation Commands\n\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-punjabi
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10](https://hu... | {"language": ["pa"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10", "model-index": [{"name": "wav2vec2-punjabi-... | kingabzpro/wav2vec2-large-xlsr-53-punjabi | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"pa",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10",
"license:apache-2.0",
"model-ind... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #pa #dataset-mozilla-foundation/common_voice_8_0 #base_model-Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-punjabi
==============================
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2101
* Wer: 0.4939
* Cer: 0.2238
#### Evaluation Commands
1. To eval... | [
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'",
"### Inference With LM",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size:... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #pa #dataset-mozilla-foundation/common_voice_8_0 #base_model-Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
... |
automatic-speech-recognition | transformers |
## Evaluation on WOLOF Test
[](https://github.com/kingabzpro/WOLOF-ASR-Wav2Vec2)
```python
import pandas as pd
from datasets import load_dataset, load_metric,Dataset
from tqdm import tqdm
import torch
import soundfile as ... | {"language": ["wo"], "license": "apache-2.0", "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | kingabzpro/wav2vec2-large-xlsr-53-wolof | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"wo",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"wo"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #wo #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
## Evaluation on WOLOF Test
![github](URL
You can check my result on Zindi, I got 8th rank in AI4D Baamtu Datamation - Automatic Speech Recognition in WOLOF
Result: 7.88 % | [
"## Evaluation on WOLOF Test\n\n![github](URL\n\nYou can check my result on Zindi, I got 8th rank in AI4D Baamtu Datamation - Automatic Speech Recognition in WOLOF\n\nResult: 7.88 %"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #automatic-speech-recognition #wo #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"## Evaluation on WOLOF Test\n\n![github](URL\n\nYou can check my result on Zindi, I got 8th rank in AI4D Baamtu Datamation - Automatic Speech Recognit... |
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