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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-shakespearify-lite
This model was trained from the t5 checkpoint on a custom dataset from [Shakescleare](https://www.litchart... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "t5-shakespearify-lite", "results": []}]} | Gorilla115/t5-shakespearify-lite | null | [
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"region:us"
] | null | 2022-08-09T18:33:07+00:00 | [] | [] | TAGS
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|
# t5-shakespearify-lite
This model was trained from the t5 checkpoint on a custom dataset from Shakescleare. This is a website shakespeare's works have been translated to modern english. This model idealizes style transforms as a translation process as we use the original english as a final translation. The dataset... | [
"# t5-shakespearify-lite\n\nThis model was trained from the t5 checkpoint on a custom dataset from Shakescleare. This is a website shakespeare's works have been translated to modern english. This model idealizes style transforms as a translation process as we use the original english as a final translation. The dat... | [
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text-to-image | null |
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Stable-Diffusion-v-1-1** was trained on 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en), followe... | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "text-to-image"], "extra_gated_prompt": "This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the model to deliberately ... | CompVis/stable-diffusion-v-1-1-original | null | [
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] | null | 2022-08-09T18:36:42+00:00 | [
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|
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The Stable-Diffusion-v-1-1 was trained on 237,000 steps at resolution '256x256' on laion2B-en, followed by
194,000 steps at resolution '512x512' on laion-high... | [
"# Stable Diffusion v1 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.\n\nThe Stable-Diffusion-v-1-1 was trained on 237,000 steps at resolution '256x256' on laion2B-en, followed by\n194,000 steps at resolution '512x512' on l... | [
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"# Stable Diffusion v1 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic ima... |
text-generation | transformers |
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" a... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | model-attribution-challenge/bloom-2b5 | null | [
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"... | null | 2022-08-09T18:38:50+00:00 | [
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... | TAGS
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========
*BigScience Large Open-science Open-access Multilingual Language Model*
-----------------------------------------------------------------------
### Model Card
Version 1.0 / 26.May.2022
Table of Contents
-----------------
1. Model Details
2. Uses
3. Training Data
4. Risks and Li... | [
"### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n--------... | [
"TAGS\n#transformers #pytorch #bloom #feature-extraction #text-generation #ak #ar #as #bm #bn #ca #code #en #es #eu #fon #fr #gu #hi #id #ig #ki #kn #lg #ln #ml #mr #ne #nso #ny #or #pa #pt #rn #rw #sn #st #sw #ta #te #tn #ts #tum #tw #ur #vi #wo #xh #yo #zh #zhs #zht #zu #arxiv-1909.08053 #arxiv-2110.02861 #arxiv-... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-misogyny-sexism-decay0.01-fr-outofdomain
This model is a fine-tuned version of [distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilbert-base-multilingual-cased-misogyny-sexism-decay0.01-fr-outofdomain", "results": []}]} | annahaz/distilbert-base-multilingual-cased-misogyny-sexism-decay0.01-fr-outofdomain | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T18:40:47+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-misogyny-sexism-decay0.01-fr-outofdomain
===========================================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.138... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-text2log-finetuned-nl-to-fol
This model is a fine-tuned version of [mrm8488/t5-small-finetuned-text2log](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-text2log-finetuned-nl-to-fol", "results": []}]} | anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T18:47:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-text2log-finetuned-nl-to-fol
===============================================
This model is a fine-tuned version of mrm8488/t5-small-finetuned-text2log on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0267
* Bleu: 36.0754
* Gen Len: 18.6964
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 200\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-ner", "results": []}]} | Jinchen/bert-base-uncased-finetuned-ner | null | [
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"optimum_graphcore",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T19:36:12+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0712
* Precision: 0.8945
* Recall: 0.9182
* F1: 0.9062
* Accuracy: 0.9793
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: IPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_s... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
tabular-classification | sklearn |
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|---------------------|----------|
... | {"library_name": "sklearn", "tags": ["sklearn", "tabular-classification", "skops"], "widget": {"structuredData": {"x0": [0.0, 0.0, 0.0], "x1": [0.0, 0.0, 0.0], "x10": [13.0, 0.0, 3.0], "x11": [15.0, 11.0, 16.0], "x12": [10.0, 16.0, 15.0], "x13": [15.0, 9.0, 14.0], "x14": [5.0, 0.0, 0.0], "x15": [0.0, 0.0, 0.0], "x16": ... | julien-c/skops-digits | null | [
"sklearn",
"joblib",
"tabular-classification",
"skops",
"region:us"
] | null | 2022-08-09T20:07:05+00:00 | [] | [] | TAGS
#sklearn #joblib #tabular-classification #skops #region-us
| Model description
=================
Intended uses & limitations
---------------------------
Training Procedure
------------------
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
### Model Plot
The model plot is below.
#sk-container-id-1 {color: black;background-co... | [
"### Hyperparameters\n\n\nThe model is trained with below hyperparameters.\n\n\n\n Click to expand",
"### Model Plot\n\n\nThe model plot is below."
] | [
"TAGS\n#sklearn #joblib #tabular-classification #skops #region-us \n",
"### Hyperparameters\n\n\nThe model is trained with below hyperparameters.\n\n\n\n Click to expand",
"### Model Plot\n\n\nThe model plot is below."
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clinical_bio_bert_ft
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentz... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "clinical_bio_bert_ft", "results": []}]} | ericntay/clinical_bio_bert_ft | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T20:25:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| clinical\_bio\_bert\_ft
=======================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2570
* F1: 0.8160
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size:... |
null | null | A basic ML model written in Julia. The purpose is to demonstrate how to Work on the HuggingFace Hub | {} | vonewman/MyFirstJuliaModel | null | [
"region:us"
] | null | 2022-08-09T20:32:09+00:00 | [] | [] | TAGS
#region-us
| A basic ML model written in Julia. The purpose is to demonstrate how to Work on the HuggingFace Hub | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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", "co... | BrianT/distilbert-base-uncased-finetuned-cola | null | [
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] | null | 2022-08-09T20:45:10+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: 0.5254
* Matthews Correlation: 0.5475
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... |
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. -->
# deberta-v3-large-finetuned-syndag-multiclass-not-gpt2-arxiv
This model is a fine-tuned version of [microsoft/deberta-v3-large](h... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-syndag-multiclass-not-gpt2-arxiv", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-syndag-multiclass-not-gpt2-arxiv | null | [
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"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
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"region:us"
] | null | 2022-08-09T21:13:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-syndag-multiclass-not-gpt2-arxiv
===========================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0272
* F1: 0.9941
* Precision: 0.9941
* R... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1235146886
- CO2 Emissions (in grams): 2.3077
## Validation Metrics
- Loss: 0.802
- Accuracy: 0.788
- Macro F1: 0.743
- Micro F1: 0.788
- Weighted F1: 0.782
- Macro Precision: 0.818
- Micro Precision: 0.788
- Weighted Precision: ... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["aujer/autotrain-data-not_interested_3"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 2.307650736568978}} | aujer/not_interested_v0 | null | [
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"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:aujer/autotrain-data-not_interested_3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T21:28:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1235146886
- CO2 Emissions (in grams): 2.3077
## Validation Metrics
- Loss: 0.802
- Accuracy: 0.788
- Macro F1: 0.743
- Micro F1: 0.788
- Weighted F1: 0.782
- Macro Precision: 0.818
- Micro Precision: 0.788
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1235146886\n- CO2 Emissions (in grams): 2.3077",
"## Validation Metrics\n\n- Loss: 0.802\n- Accuracy: 0.788\n- Macro F1: 0.743\n- Micro F1: 0.788\n- Weighted F1: 0.782\n- Macro Precision: 0.818\n- Micro Precision: 0.788\n-... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1235146886\n- CO2 Emis... |
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. -->
# Bio_ClinicalBERT_fold_3_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_3_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_3_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T21:59:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_3\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0585
* F1: 0.7952
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-classification | transformers | # tner/roberta-large-wnut2017
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
... | {"datasets": ["wnut2017"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-wnut2017", "results": [{"task": {"type": "token-clas... | tner/roberta-large-wnut2017 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:12:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-wnut2017
This model is a fine-tuned version of roberta-large on the
tner/wnut2017 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.5375139977603584
- Precision (micro): 0.... | [
"# tner/roberta-large-wnut2017\n\nThis model is a fine-tuned version of roberta-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.5375139977603584\n- Precision... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-wnut2017\n\nThis model is a fine-tuned version of roberta-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done via T-NER's hyper... |
token-classification | transformers | # tner/deberta-v3-large-wnut2017
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-paramete... | {"datasets": ["tner/wnut2017"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-wnut2017", "results": [{"task": {"type": "to... | tner/deberta-v3-large-wnut2017 | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:14:32+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-wnut2017
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/wnut2017 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.5047353760445682
- Preci... | [
"# tner/deberta-v3-large-wnut2017\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.5047353760445... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-wnut2017\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/wnut2017 dataset.\nModel fine-tuning i... |
token-classification | transformers | # tner/roberta-large-conll2003
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/conll2003](https://huggingface.co/datasets/tner/conll2003) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the reposito... | {"datasets": ["tner/conll2003"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-conll2003", "results": [{"task": {"type": "tok... | tner/roberta-large-conll2003 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:19:06+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-conll2003
This model is a fine-tuned version of roberta-large on the
tner/conll2003 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.924769027716674
- Precision (micro): 0... | [
"# tner/roberta-large-conll2003\n\nThis model is a fine-tuned version of roberta-large on the \ntner/conll2003 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.924769027716674\n- Precisio... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-conll2003\n\nThis model is a fine-tuned version of roberta-large on the \ntner/conll2003 dataset.\nModel fine-tuning is done via T-NER... |
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. -->
# Bio_ClinicalBERT_fold_4_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_4_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_4_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:22:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_4\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7349
* F1: 0.8052
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-classification | transformers | # tner/bertweet-large-wnut2017
This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see ... | {"datasets": ["tner/wnut2017"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/bertweet-large-wnut2017", "results": [{"task": {"type": "toke... | tner/bertweet-large-wnut2017 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:25:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/bertweet-large-wnut2017
This model is a fine-tuned version of vinai/bertweet-large on the
tner/wnut2017 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.5302273987798114
- Precision (mi... | [
"# tner/bertweet-large-wnut2017\n\nThis model is a fine-tuned version of vinai/bertweet-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.5302273987798114\n- P... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/bertweet-large-wnut2017\n\nThis model is a fine-tuned version of vinai/bertweet-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done via ... |
token-classification | transformers | # tner/deberta-large-wnut2017
This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search ... | {"datasets": ["tner/wnut2017"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-large-wnut2017", "results": [{"task": {"type": "token... | tner/deberta-large-wnut2017 | null | [
"transformers",
"pytorch",
"deberta",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:25:51+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #token-classification #dataset-tner/wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-large-wnut2017
This model is a fine-tuned version of microsoft/deberta-large on the
tner/wnut2017 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.5105386416861827
- Precision (... | [
"# tner/deberta-large-wnut2017\n\nThis model is a fine-tuned version of microsoft/deberta-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.5105386416861827\n-... | [
"TAGS\n#transformers #pytorch #deberta #token-classification #dataset-tner/wnut2017 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-large-wnut2017\n\nThis model is a fine-tuned version of microsoft/deberta-large on the \ntner/wnut2017 dataset.\nModel fine-tuning is done vi... |
token-classification | transformers | # tner/deberta-v3-large-conll2003
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/conll2003](https://huggingface.co/datasets/tner/conll2003) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-param... | {"datasets": ["tner/conll2003"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-conll2003", "results": [{"task": {"type": "... | tner/deberta-v3-large-conll2003 | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:28:29+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-conll2003
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/conll2003 dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.9222388190844389
- Pre... | [
"# tner/deberta-v3-large-conll2003\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/conll2003 dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.92223881908... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-conll2003\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/conll2003 dataset.\nModel fine-tunin... |
token-classification | transformers | # tner/deberta-v3-large-bc5cdr
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter sear... | {"datasets": ["tner/bc5cdr"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-bc5cdr", "results": [{"task": {"type": "token-... | tner/deberta-v3-large-bc5cdr | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/bc5cdr",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:31:56+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/bc5cdr #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-bc5cdr
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/bc5cdr dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8902493653874869
- Precision... | [
"# tner/deberta-v3-large-bc5cdr\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/bc5cdr dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8902493653874869\... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/bc5cdr #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-bc5cdr\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/bc5cdr dataset.\nModel fine-tuning is done... |
token-classification | transformers | # tner/roberta-large-bc5cdr
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for mo... | {"datasets": ["tner/bc5cdr"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-bc5cdr", "results": [{"task": {"type": "token-cla... | tner/roberta-large-bc5cdr | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/bc5cdr",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:32:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/bc5cdr #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-bc5cdr
This model is a fine-tuned version of roberta-large on the
tner/bc5cdr dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8840696387239609
- Precision (micro): 0.8728... | [
"# tner/roberta-large-bc5cdr\n\nThis model is a fine-tuned version of roberta-large on the \ntner/bc5cdr dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8840696387239609\n- Precision (mi... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/bc5cdr #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-bc5cdr\n\nThis model is a fine-tuned version of roberta-large on the \ntner/bc5cdr dataset.\nModel fine-tuning is done via T-NER's hyper-... |
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. -->
# Bio_ClinicalBERT_fold_5_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_5_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_5_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T22:45:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_5\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0233
* F1: 0.7849
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT_fold_6_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_6_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_6_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T23:07:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_6\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7302
* F1: 0.8128
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
token-classification | transformers | # tner/roberta-large-btc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/btc](https://huggingface.co/datasets/tner/btc) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail... | {"datasets": ["tner/btc"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-btc", "results": [{"task": {"type": "token-classific... | tner/roberta-large-btc | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/btc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T23:10:29+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/btc #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-btc
This model is a fine-tuned version of roberta-large on the
tner/btc dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8367557645979121
- Precision (micro): 0.8401290025... | [
"# tner/roberta-large-btc\n\nThis model is a fine-tuned version of roberta-large on the \ntner/btc dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.8367557645979121\n- Precision (micro): ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/btc #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-btc\n\nThis model is a fine-tuned version of roberta-large on the \ntner/btc dataset.\nModel fine-tuning is done via T-NER's hyper-parameter... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | Tstarshak/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-09T23:20:23+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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. -->
# Bio_ClinicalBERT_fold_7_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_7_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_7_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T23:30:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_7\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9612
* F1: 0.7939
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# oddood/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkn... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "oddood/bert-finetuned-squad", "results": []}]} | oddood/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T23:35:10+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| oddood/bert-finetuned-squad
===========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5679
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 16638, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
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. -->
# distilled-mt5-small-00001b
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-00001b", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-00001b | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T23:51:11+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-00001b
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8994
- Bleu: 7.5838
- Gen Len: 45.058
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-00001b\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8994\n- Bleu: 7.5838\n- Gen Len: 45.058",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-00001b\n\nThis model is a fine-tuned version of google/mt5-small on... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT_fold_8_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_8_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_8_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T23:53:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_8\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8248
* F1: 0.8010
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# distilled-mt5-small-1t9901
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-1t9901", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-1t9901 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T00:05:23+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-1t9901
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9223
- Bleu: 0.4773
- Gen Len: 51.3902
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-1t9901\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.9223\n- Bleu: 0.4773\n- Gen Len: 51.3902",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-1t9901\n\nThis model is a fine-tuned version of google/mt5-small on... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT_fold_9_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_9_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_9_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T00:16:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_9\_ternary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0189
* F1: 0.7905
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT_fold_10_ternary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_10_ternary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_10_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T00:40:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_10\_ternary\_v1
========================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0706
* F1: 0.7748
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
fill-mask | transformers | Note: this model is deprecated, please use https://huggingface.co/songlab/gpn-brassicales | {"license": "mit"} | songlab/gpn-arabidopsis | null | [
"transformers",
"pytorch",
"ConvNet",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T01:08:13+00:00 | [] | [] | TAGS
#transformers #pytorch #ConvNet #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Note: this model is deprecated, please use URL | [] | [
"TAGS\n#transformers #pytorch #ConvNet #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
#harry pitter dialoGPT model | {"tags": ["conversational"]} | noiseBase/DialoGPT-small-HarryPotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T01:12:58+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#harry pitter dialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-010099_1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099_1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "r... | Lvxue/distilled-mt5-small-010099_1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T01:20:53+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099_1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8040
- Bleu: 7.3454
- Gen Len: 44.8149
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# distilled-mt5-small-010099_1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8040\n- Bleu: 7.3454\n- Gen Len: 44.8149",
"## Model description\n\nMore information needed",
"## Intended uses & limitations... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099_1\n\nThis model is a fine-tuned version of google/mt5-small ... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rebolforces/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | rebolforces/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-10T01:23:39+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# distilled-mt5-small-1b0000
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-1b0000", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-1b0000 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T01:23:44+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-1b0000
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7760
- Bleu: 1.1101
- Gen Len: 99.5898
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-1b0000\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.7760\n- Bleu: 1.1101\n- Gen Len: 99.5898",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-1b0000\n\nThis model is a fine-tuned version of google/mt5-small on... |
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. -->
# distilled-mt5-small-010099_8
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099_8", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "r... | Lvxue/distilled-mt5-small-010099_8 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T01:24:27+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099_8
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9641
- Bleu: 6.231
- Gen Len: 50.1911
## Model description
More information needed
## Intended uses & limitations
More information ... | [
"# distilled-mt5-small-010099_8\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.9641\n- Bleu: 6.231\n- Gen Len: 50.1911",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099_8\n\nThis model is a fine-tuned version of google/mt5-small ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-my_dear_watson2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-r... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-my_dear_watson2", "results": []}]} | SmartPy/xlm-roberta-base-finetuned-my_dear_watson2 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T01:49:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-base-finetuned-my_dear_watson2
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# xlm-roberta-base-finetuned-my_dear_watson2\n\nThis model is a fine-tuned version of xlm-roberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-base-finetuned-my_dear_watson2\n\nThis model is a fine-tuned version of xlm-roberta-base on the None dataset.",
"## Model description... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | yokoe/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T02:13:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1365
* F1: 0.8649
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# finetuning-sentiment-model-4000-samples_en
This model is a fine-tuned version of [zboxi7/finetuning-sentiment-model-3000-samples... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuning-sentiment-model-4000-samples_en", "results": []}]} | zboxi7/finetuning-sentiment-model-4000-samples_en | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T02:25:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-4000-samples_en
This model is a fine-tuned version of zboxi7/finetuning-sentiment-model-3000-samples_fr on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3887
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# finetuning-sentiment-model-4000-samples_en\n\nThis model is a fine-tuned version of zboxi7/finetuning-sentiment-model-3000-samples_fr on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3887",
"## Model description\n\nMore information needed",
"## Intended uses & limitat... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-4000-samples_en\n\nThis model is a fine-tuned version of zboxi7/finetuning-sentiment-model-3000-samples_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | yokoe/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T04:00:36+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1608
* F1: 0.8593
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on th... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-test", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-en... | Lvxue/distilled-mt5-small-test | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T04:05:11+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-test
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8241
- Bleu: 7.5082
- Gen Len: 44.0405
## Model description
More information needed
## Intended uses & limitations
More information nee... | [
"# distilled-mt5-small-test\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8241\n- Bleu: 7.5082\n- Gen Len: 44.0405",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\n... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-test\n\nThis model is a fine-tuned version of google/m... |
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. -->
# distilled-mt5-small-010099-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": ... | Lvxue/distilled-mt5-small-010099-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T04:14:01+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8127
- Bleu: 7.735
- Gen Len: 44.5453
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# distilled-mt5-small-010099-0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8127\n- Bleu: 7.735\n- Gen Len: 44.5453",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-0.5\n\nThis model is a fine-tuned version of google/mt5-smal... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | jaybeeja/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-10T04:14:20+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# distilled-mt5-small-010099-0.75
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-0.75", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args":... | Lvxue/distilled-mt5-small-010099-0.75 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T04:16:24+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-0.75
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8048
- Bleu: 7.3342
- Gen Len: 44.5718
## Model description
More information needed
## Intended uses & limitations
More informat... | [
"# distilled-mt5-small-010099-0.75\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8048\n- Bleu: 7.3342\n- Gen Len: 44.5718",
"## Model description\n\nMore information needed",
"## Intended uses & limitati... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-0.75\n\nThis model is a fine-tuned version of google/mt5-sma... |
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. -->
# distilled-mt5-small-010099-1.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-1.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": ... | Lvxue/distilled-mt5-small-010099-1.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T04:17:03+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-1.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8969
- Bleu: 7.1585
- Gen Len: 46.8959
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# distilled-mt5-small-010099-1.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8969\n- Bleu: 7.1585\n- Gen Len: 46.8959",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-1.5\n\nThis model is a fine-tuned version of google/mt5-smal... |
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. -->
# distilled-mt5-small-010099-5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "r... | Lvxue/distilled-mt5-small-010099-5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T04:19:29+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9590
- Bleu: 6.2032
- Gen Len: 51.0205
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# distilled-mt5-small-010099-5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.9590\n- Bleu: 6.2032\n- Gen Len: 51.0205",
"## Model description\n\nMore information needed",
"## Intended uses & limitations... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-5\n\nThis model is a fine-tuned version of google/mt5-small ... |
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. -->
# distilled-mt5-small-010099-10
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-10", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "... | Lvxue/distilled-mt5-small-010099-10 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T04:21:18+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-10
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9685
- Bleu: 6.1705
- Gen Len: 50.5663
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# distilled-mt5-small-010099-10\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.9685\n- Bleu: 6.1705\n- Gen Len: 50.5663",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-10\n\nThis model is a fine-tuned version of google/mt5-small... |
null | null | rebecca ferguson beautiful girl lord of the ring armor sword fight in valley 3d 4k Alex Lazar artstation
| {} | piasinga/nude | null | [
"region:us"
] | null | 2022-08-10T04:33:51+00:00 | [] | [] | TAGS
#region-us
| rebecca ferguson beautiful girl lord of the ring armor sword fight in valley 3d 4k Alex Lazar artstation
| [] | [
"TAGS\n#region-us \n"
] |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | AdShenoy/Bart_summarizer | null | [
"fastai",
"region:us"
] | null | 2022-08-10T05:34:09+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dna_bert_3_1000seq-finetuned
This model is a fine-tuned version of [armheb/DNA_bert_3](https://huggingface.co/armheb/DNA_bert_3)... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dna_bert_3_1000seq-finetuned", "results": []}]} | Mozart-coder/dna_bert_3_1000seq-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T05:51:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| dna\_bert\_3\_1000seq-finetuned
===============================
This model is a fine-tuned version of armheb/DNA\_bert\_3 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4684
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: 100\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_si... |
token-classification | flair | ## English NER in Flair (Ontonotes fast model)
F1-Score: **84.3** (Ontonotes)
Predicts 2 tags:
| tag | meaning |
|---------------------------------|-----------|
| SKILL | skill name |
| EXPERIENCE | year of experience |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/)... | {"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Delphi SQL developer", "example_title": "Example 1"}, {"text": "Searching for new opportunities as Junior Node.js JavaScript backend developer. Over 15 years of experience in different IT areas. Experience with: ... | kaliani/flair-ner-skill | null | [
"flair",
"pytorch",
"bert",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] | null | 2022-08-10T06:07:20+00:00 | [] | [
"en"
] | TAGS
#flair #pytorch #bert #token-classification #sequence-tagger-model #en #region-us
| English NER in Flair (Ontonotes fast model)
-------------------------------------------
F1-Score: 84.3 (Ontonotes)
Predicts 2 tags:
| [] | [
"TAGS\n#flair #pytorch #bert #token-classification #sequence-tagger-model #en #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sd-ner-v2
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/mi... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["source_data_nlp"], "metrics": ["precision", "recall", "f1", {"name": "sd-ner-v2", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "source_data_nlp", "type": "source_data_nlp", "args": "NER"}, ... | EMBO/sd-ner-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:source_data_nlp",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T06:30:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sd-ner-v2
=========
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the source\_data\_nlp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1551
* Accuracy Score: 0.9513
* Precision: 0.8030
* Recall: 0.8378
* F1: 0.8200
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adafactor\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2.0",
"### Training results",
"### Framework ve... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000... |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1235946916
- CO2 Emissions (in grams): 292.2926
## Validation Metrics
- Loss: 2.912
- Rouge1: 23.807
- Rouge2: 10.396
- RougeL: 21.142
- RougeLsum: 21.101
- Gen Len: 13.017
## Usage
You can use cURL to access this model:
```
$ curl -X POST... | {"language": ["unk"], "tags": ["autotrain", "summarization"], "datasets": ["WLD/autotrain-data-Sum"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 292.2926477361632}} | WLD/autotrain-Sum-1235946916 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:WLD/autotrain-data-Sum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T06:36:12+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #summarization #unk #dataset-WLD/autotrain-data-Sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1235946916
- CO2 Emissions (in grams): 292.2926
## Validation Metrics
- Loss: 2.912
- Rouge1: 23.807
- Rouge2: 10.396
- RougeL: 21.142
- RougeLsum: 21.101
- Gen Len: 13.017
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1235946916\n- CO2 Emissions (in grams): 292.2926",
"## Validation Metrics\n\n- Loss: 2.912\n- Rouge1: 23.807\n- Rouge2: 10.396\n- RougeL: 21.142\n- RougeLsum: 21.101\n- Gen Len: 13.017",
"## Usage\n\nYou can use cURL to access this m... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #summarization #unk #dataset-WLD/autotrain-data-Sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1235946916\n- CO2 Emissions (in g... |
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. -->
# output
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the im... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "output", "results": []}]} | MelikeDulkadir/output | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T06:36:20+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# output
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
## Model description
This model gives the result of LABEL_1 if the given sentences are positive, and LABEL_0 if they are negative, together with the calculated probabality values.
## Intended uses & limitations
More inf... | [
"# output\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.",
"## Model description\n\nThis model gives the result of LABEL_1 if the given sentences are positive, and LABEL_0 if they are negative, together with the calculated probabality values.",
"## Intended uses & limitati... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# output\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.",
"## Model description\n\nThis mod... |
translation | 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. -->
# Ts-En_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ts-en](https://huggingface.co/Helsinki-NLP/opus-mt-ts-e... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Ts-En_update", "results": []}]} | kabelomalapane/Ts-En_update | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T06:44:34+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Ts-En\_update
=============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ts-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2030
* Bleu: 44.6835
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: 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #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\\_batc... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | BekirTaha/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-10T07:02:20+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
automatic-speech-recognition | transformers |
# Irish-Gaelic Automatic Speech Recognition
This is the model for Irish ASR. It has been trained on the Common-voice dataset and living Irish audio dataset. The Common-voice code for the Irish language is ga-IE. From the Common voice dataset, all the Validated audio clips and all the living audio clips were taken int... | {"language": "ga", "tags": ["audio", "automatic-speech-recognition", "ga-IE", "speech", "Irish", "Gaelic"], "datasets": ["common_voice", "living-audio-Irish"], "metrics": ["wer"], "model-index": [{"name": "Wav2vec 2.0 large 300m XLS-R", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Sp... | Aditya3107/wav2vec2-large-xls-r-1b-ga-ie | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"ga-IE",
"speech",
"Irish",
"Gaelic",
"ga",
"dataset:common_voice",
"dataset:living-audio-Irish",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T07:12:00+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #ga-IE #speech #Irish #Gaelic #ga #dataset-common_voice #dataset-living-audio-Irish #model-index #endpoints_compatible #region-us
|
# Irish-Gaelic Automatic Speech Recognition
This is the model for Irish ASR. It has been trained on the Common-voice dataset and living Irish audio dataset. The Common-voice code for the Irish language is ga-IE. From the Common voice dataset, all the Validated audio clips and all the living audio clips were taken int... | [
"# Irish-Gaelic Automatic Speech Recognition\n\nThis is the model for Irish ASR. It has been trained on the Common-voice dataset and living Irish audio dataset. The Common-voice code for the Irish language is ga-IE. From the Common voice dataset, all the Validated audio clips and all the living audio clips were tak... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #audio #ga-IE #speech #Irish #Gaelic #ga #dataset-common_voice #dataset-living-audio-Irish #model-index #endpoints_compatible #region-us \n",
"# Irish-Gaelic Automatic Speech Recognition\n\nThis is the model for Irish ASR. It has b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | jmurphy97/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T07:25:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2308
* Accuracy: 0.9195
* F1: 0.9195
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# XLM-roberta-finetuned
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown ... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "XLM-roberta-finetuned", "results": []}]} | Taoseef/XLM-roberta-finetuned | null | [
"transformers",
"tf",
"xlm-roberta",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T07:28:09+00:00 | [] | [] | TAGS
#transformers #tf #xlm-roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-roberta-finetuned
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information n... | [
"# XLM-roberta-finetuned\n\nThis model is a fine-tuned version of xlm-roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #tf #xlm-roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-roberta-finetuned\n\nThis model is a fine-tuned version of xlm-roberta-base on an unknown dataset.\nIt achieves the following results on the eva... |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | karan21/DialoGPT-medium-rickandmorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T07:29:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
unconditional-image-generation | null |
# Model info
Project [fbanimegan](https://github.com/SkyTNT/fbanimegan)
### fbanime.pkl
StyleGan2 model trained with official [StyleGan3](https://github.com/NVlabs/stylegan3).
But I modified the code (networks_stylegan2.py and dataset.py) to support non-square resolutions.
FID: 1.4
### fbanime_fp32.pkl
fp32 vers... | {"license": "apache-2.0", "tags": ["unconditional-image-generation"]} | skytnt/fbanime-gan | null | [
"onnx",
"unconditional-image-generation",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-10T07:32:22+00:00 | [] | [] | TAGS
#onnx #unconditional-image-generation #license-apache-2.0 #has_space #region-us
|
# Model info
Project fbanimegan
### URL
StyleGan2 model trained with official StyleGan3.
But I modified the code (networks_stylegan2.py and URL) to support non-square resolutions.
FID: 1.4
### fbanime_fp32.pkl
fp32 version of URL
Note: Fp16 version (URL) only works on gpu. And fp32 version works on gpu and cpu.... | [
"# Model info\n\nProject fbanimegan",
"### URL\n\nStyleGan2 model trained with official StyleGan3.\nBut I modified the code (networks_stylegan2.py and URL) to support non-square resolutions.\n\nFID: 1.4",
"### fbanime_fp32.pkl\n\nfp32 version of URL\n\nNote: Fp16 version (URL) only works on gpu. And fp32 versio... | [
"TAGS\n#onnx #unconditional-image-generation #license-apache-2.0 #has_space #region-us \n",
"# Model info\n\nProject fbanimegan",
"### URL\n\nStyleGan2 model trained with official StyleGan3.\nBut I modified the code (networks_stylegan2.py and URL) to support non-square resolutions.\n\nFID: 1.4",
"### fbanime_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | amitkayal/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T07:46:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9288
* Recall: 0.9388
* F1: 0.9338
* Accuracy: 0.9840
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sd-geneprod-roles-v2
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunag... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["source_data_nlp"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "sd-geneprod-roles-v2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "source_data_nlp", "type": ... | EMBO/sd-geneprod-roles-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:source_data_nlp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T08:04:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sd-geneprod-roles-v2
====================
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-large on the source\_data\_nlp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0136
* Accuracy Score: 0.9950
* Precision: 0.9228
* Recall: 0.9288
* F1: 0.9258
Model description
---... | [
"### 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: 256\n* seed: 42\n* optimizer: Adafactor\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Training results",
"### Framework ver... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset":... | phamvanlinh143/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T08:26:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0599
* Precision: 0.9371
* Recall: 0.9530
* F1: 0.9450
* Accuracy: 0.9865
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-bert-base-cased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | mvicentel/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-10T08:35:23+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
token-classification | transformers | # tner/roberta-large-tweebank-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the... | {"datasets": ["tner/tweebank_ner"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/roberta-large-tweebank-ner", "results": [{"task": {"type"... | tner/roberta-large-tweebank-ner | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweebank_ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T09:03:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #dataset-tner/tweebank_ner #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/roberta-large-tweebank-ner
This model is a fine-tuned version of roberta-large on the
tner/tweebank_ner dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7439490445859872
- Precision (mi... | [
"# tner/roberta-large-tweebank-ner\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweebank_ner dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.7439490445859872\n- P... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #dataset-tner/tweebank_ner #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/roberta-large-tweebank-ner\n\nThis model is a fine-tuned version of roberta-large on the \ntner/tweebank_ner dataset.\nModel fine-tuning is done ... |
token-classification | transformers | # tner/deberta-v3-large-tweebank-ner
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hy... | {"datasets": ["tner/tweebank_ner"], "metrics": ["f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Jacob Collier is a Grammy awarded artist from England.", "example_title": "NER Example 1"}], "model-index": [{"name": "tner/deberta-v3-large-tweebank-ner", "results": [{"task": {"ty... | tner/deberta-v3-large-tweebank-ner | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/tweebank_ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T09:07:10+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/tweebank_ner #model-index #autotrain_compatible #endpoints_compatible #region-us
| # tner/deberta-v3-large-tweebank-ner
This model is a fine-tuned version of microsoft/deberta-v3-large on the
tner/tweebank_ner dataset.
Model fine-tuning is done via T-NER's hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7253474520185308... | [
"# tner/deberta-v3-large-tweebank-ner\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/tweebank_ner dataset.\nModel fine-tuning is done via T-NER's hyper-parameter search (see the repository\nfor more detail). It achieves the following results on the test set:\n- F1 (micro): 0.72534... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #dataset-tner/tweebank_ner #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# tner/deberta-v3-large-tweebank-ner\n\nThis model is a fine-tuned version of microsoft/deberta-v3-large on the \ntner/tweebank_ner dataset.\nModel f... |
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. -->
# xlnet-base-cased_fold_1_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_1_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_1_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T09:09:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_1\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7812
* F1: 0.8161
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sd-panelization-v2
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["source_data_nlp"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "sd-panelization-v2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "source_data_nlp", "type": "s... | EMBO/sd-panelization-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:source_data_nlp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T09:27:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sd-panelization-v2
==================
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-large on the source\_data\_nlp dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0050
* Accuracy Score: 0.9982
* Precision: 0.9134
* Recall: 0.9495
* F1: 0.9311
Model description
-------... | [
"### 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: 256\n* seed: 42\n* optimizer: Adafactor\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Training results",
"### Framework ver... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# xlnet-base-cased_fold_2_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_2_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_2_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T09:39:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_2\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8748
* F1: 0.8066
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | sofiaoliveira/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-10T10:04:18+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
image-classification | transformers |
# ConvNext-tiny-finetuned-cifar10 (tiny-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt).
Convnext ti... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["cifar10"]} | ahsanjavid/convnext-tiny-finetuned-cifar10 | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"vision",
"dataset:cifar10",
"arxiv:2201.03545",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T10:09:01+00:00 | [
"2201.03545"
] | [] | TAGS
#transformers #pytorch #convnext #image-classification #vision #dataset-cifar10 #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNext-tiny-finetuned-cifar10 (tiny-sized model)
ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.
Convnext tiny finetuned on cifar 10 dataset. Which has ten classes.
Disclaimer: The team relea... | [
"# ConvNext-tiny-finetuned-cifar10 (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository.\nConvnext tiny finetuned on cifar 10 dataset. Which has ten classes.\n\nDisclaimer: The t... | [
"TAGS\n#transformers #pytorch #convnext #image-classification #vision #dataset-cifar10 #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNext-tiny-finetuned-cifar10 (tiny-sized model) \n\nConvNeXT model trained on ImageNet-1k at resolution 224x224. It was int... |
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. -->
# xlnet-base-cased_fold_3_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_3_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_3_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T10:09:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_3\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8649
* F1: 0.8044
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_norm300
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm300", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm300 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T10:25:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm300
=================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0940
* Mse: 4.3760
* Mae: 1.4084
* R2: 0.4625
* Accuracy: 0.3517... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers | ---
About :
This model can be used for text summarization.
The dataset on which it was fine tuned consisted of 10,323 articles.
The Data Fields :
- "Headline" : title of the article
- "articleBody" : the main article content
- "source" : the link to the readmore page.
The data splits were :
- Train : 8258.... | {"license": "afl-3.0"} | AkashKhamkar/InSumT510k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T10:27:49+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
About :
This model can be used for text summarization.
The dataset on which it was fine tuned consisted of 10,323 articles.
The Data Fields :
- "Headline" : title of the article
- "articleBody" : the main article content
- "source" : the link to the readmore page.
The data splits were :
- Train : 8258.... | [
"### How to use along with pipeline\n\n\nlanguage:\n- English\n \nlibrary_name: Pytorch \n\ntags:\n- Summarization \n- T5-base\n- Conditional Modelling \n-"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use along with pipeline\n\n\nlanguage:\n- English\n \nlibrary_name: Pytorch \n\ntags:\n- Summarization \n- T5-base\n- Conditional Modelling ... |
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. -->
# xlnet-base-cased_fold_4_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_4_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_4_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T10:39:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_4\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5724
* F1: 0.8315
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | BekirTaha/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-10T10:56:42+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
zero-shot-image-classification | generic |
# Fork of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) for a `zero-sho-image-classification` Inference endpoint.
This repository implements a `custom` task for `zero-shot-image-classification` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.p... | {"library_name": "generic", "tags": ["vision", "zero-shot-image-classification", "endpoints-template"]} | philschmid/clip-zero-shot-image-classification | null | [
"generic",
"pytorch",
"clip",
"vision",
"zero-shot-image-classification",
"endpoints-template",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-10T10:57:55+00:00 | [] | [] | TAGS
#generic #pytorch #clip #vision #zero-shot-image-classification #endpoints-template #endpoints_compatible #has_space #region-us
|
# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.
This repository implements a 'custom' task for 'zero-shot-image-classification' for Inference Endpoints. The code for the customized pipeline is in the URL.
To use deploy this model a an Inference Endpoint you have to s... | [
"# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.\n\nThis repository implements a 'custom' task for 'zero-shot-image-classification' for Inference Endpoints. The code for the customized pipeline is in the URL.\n\nTo use deploy this model a an Inference Endpoint you h... | [
"TAGS\n#generic #pytorch #clip #vision #zero-shot-image-classification #endpoints-template #endpoints_compatible #has_space #region-us \n",
"# Fork of openai/clip-vit-base-patch32 for a 'zero-sho-image-classification' Inference endpoint.\n\nThis repository implements a 'custom' task for 'zero-shot-image-classific... |
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. -->
# xlnet-base-cased_fold_5_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_5_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_5_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T11:09:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_5\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7395
* F1: 0.8206
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
translation | 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. -->
# En-Tn_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-tn](https://huggingface.co/Helsinki-NLP/opus-mt-en-t... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Tn_update", "results": []}]} | kabelomalapane/En-Tn_update | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T11:16:07+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Tn\_update
=============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-tn on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.13002
* Bleu: 39.1470
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: 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #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\\_batc... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](h... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T11:23:04+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb
====================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.6698
* Validation Loss: 4.3501
* Epo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
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. -->
# categorization-finetuned-20220721-164940-distilled-20220810-123313
This model is a fine-tuned version of [carted-nlp/categorizat... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "categorization-finetuned-20220721-164940-distilled-20220810-123313", "results": []}]} | carted-nlp/categorization-finetuned-20220721-164940-distilled-20220810-123313 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T11:35:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| categorization-finetuned-20220721-164940-distilled-20220810-123313
==================================================================
This model is a fine-tuned version of carted-nlp/categorization-finetuned-20220721-164940 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0787... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 314\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 64\n* eval... |
reinforcement-learning | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'ppo'... | {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"... | workRL/testppo | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-10T11:35:45+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL\n \n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n To learn to code your own PPO agent and 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. -->
# xlnet-base-cased_fold_6_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_6_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_6_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T11:39:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_6\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6214
* F1: 0.8352
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-to-image | null |
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Stable-Diffusion-v-1-2** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-1](https:/steps/huggingface.co/CompVis/stable-diffusio... | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "text-to-image"], "extra_gated_prompt": "This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the model to deliberately ... | CompVis/stable-diffusion-v-1-2-original | null | [
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:2207.12598",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"has_space",
"region:us"
] | null | 2022-08-10T11:40:54+00:00 | [
"2112.10752",
"2103.00020",
"2205.11487",
"2207.12598",
"1910.09700"
] | [] | TAGS
#stable-diffusion #text-to-image #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-2207.12598 #arxiv-1910.09700 #license-creativeml-openrail-m #has_space #region-us
|
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The Stable-Diffusion-v-1-2 checkpoint was initialized with the weights of the Stable-Diffusion-v-1-1
checkpoint and subsequently fine-tuned on 515,000 steps ... | [
"# Stable Diffusion v1 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.\n\nThe Stable-Diffusion-v-1-2 checkpoint was initialized with the weights of the Stable-Diffusion-v-1-1 \ncheckpoint and subsequently fine-tuned on 515,0... | [
"TAGS\n#stable-diffusion #text-to-image #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-2207.12598 #arxiv-1910.09700 #license-creativeml-openrail-m #has_space #region-us \n",
"# Stable Diffusion v1 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating photo-re... |
text-to-image | stable-diffusion |
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Stable-Diffusion-v-1-3** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion... | {"license": "creativeml-openrail-m", "library_name": "stable-diffusion", "tags": ["stable-diffusion", "text-to-image"], "extra_gated_prompt": "This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You ... | CompVis/stable-diffusion-v-1-3-original | null | [
"stable-diffusion",
"pytorch",
"clip",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-08-10T11:42:11+00:00 | [
"2207.12598",
"2112.10752",
"2103.00020",
"2205.11487",
"1910.09700"
] | [] | TAGS
#stable-diffusion #pytorch #clip #text-to-image #arxiv-2207.12598 #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-1910.09700 #license-creativeml-openrail-m #region-us
|
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The Stable-Diffusion-v-1-3 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2
checkpoint and subsequently fine-tuned on 195,000 steps a... | [
"# Stable Diffusion v1 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.\n\nThe Stable-Diffusion-v-1-3 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 \ncheckpoint and subsequently fine-tuned on 195,00... | [
"TAGS\n#stable-diffusion #pytorch #clip #text-to-image #arxiv-2207.12598 #arxiv-2112.10752 #arxiv-2103.00020 #arxiv-2205.11487 #arxiv-1910.09700 #license-creativeml-openrail-m #region-us \n",
"# Stable Diffusion v1 Model Card\n\nStable Diffusion is a latent text-to-image diffusion model capable of generating phot... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-fr-outofdomain
This model is a fine-tuned version of [distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-fr-outofdomain", "results": []}]} | annahaz/distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-fr-outofdomain | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T11:42:16+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-misogyny-sexism-decay0.05-fr-outofdomain
===========================================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.992... | [
"### 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 #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# distilled-mt5-small-010099-0.2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-0.2", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": ... | Lvxue/distilled-mt5-small-010099-0.2 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T11:51:32+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-0.2
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8513
- Bleu: 7.5783
- Gen Len: 45.037
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# distilled-mt5-small-010099-0.2\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8513\n- Bleu: 7.5783\n- Gen Len: 45.037",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-0.2\n\nThis model is a fine-tuned version of google/mt5-smal... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relb... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"... | research-backup/roberta-large-conceptnet-average-no-mask-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T12:02:46+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset... | [
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Questi... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning... |
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. -->
# distilled-mt5-small-010099-0.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-0.25", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args":... | Lvxue/distilled-mt5-small-010099-0.25 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-10T12:06:09+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-0.25
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8387
- Bleu: 7.611
- Gen Len: 44.8304
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# distilled-mt5-small-010099-0.25\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8387\n- Bleu: 7.611\n- Gen Len: 44.8304",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-0.25\n\nThis model is a fine-tuned version of google/mt5-sma... |
text-to-image | null | # Stable Diffusion
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under [Model Access]... | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "text-to-image"], "inference": false} | CompVis/stable-diffusion | null | [
"stable-diffusion",
"text-to-image",
"arxiv:2207.12598",
"license:creativeml-openrail-m",
"has_space",
"region:us"
] | null | 2022-08-10T12:09:19+00:00 | [
"2207.12598"
] | [] | TAGS
#stable-diffusion #text-to-image #arxiv-2207.12598 #license-creativeml-openrail-m #has_space #region-us
| Stable Diffusion
================
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed unde... | [
"### Model Access\n\n\nEach checkpoint can be used both with Hugging Face's Diffusers library or the original Stable Diffusion GitHub repository. Note that you have to *\"click-request\"* them on each respective model repository.",
"### Demo\n\n\nTo quickly try out the model, you can try out the Stable Diffusion ... | [
"TAGS\n#stable-diffusion #text-to-image #arxiv-2207.12598 #license-creativeml-openrail-m #has_space #region-us \n",
"### Model Access\n\n\nEach checkpoint can be used both with Hugging Face's Diffusers library or the original Stable Diffusion GitHub repository. Note that you have to *\"click-request\"* them on ea... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased]... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T12:10:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m
======================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8764
* Validation Loss: 2.7682
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
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. -->
# xlnet-base-cased_fold_7_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_7_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_7_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T12:10:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_7\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7774
* F1: 0.8111
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased_fold_8_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlnet-base-cased_fold_8_binary_v1", "results": []}]} | elopezlopez/xlnet-base-cased_fold_8_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T12:41:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased\_fold\_8\_binary\_v1
=====================================
This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5333
* F1: 0.8407
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-flowers-128-2
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hugg... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/cats", "metrics": []} | rdruce/ddpm-flowers-128-2 | null | [
"diffusers",
"en",
"dataset:huggan/cats",
"license:apache-2.0",
"diffusers:ImageDDPMPipeline",
"region:us"
] | null | 2022-08-10T12:46:48+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-huggan/cats #license-apache-2.0 #diffusers-ImageDDPMPipeline #region-us
|
# ddpm-flowers-128-2
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/cats' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe... | [
"# ddpm-flowers-128-2",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/cats' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Train... | [
"TAGS\n#diffusers #en #dataset-huggan/cats #license-apache-2.0 #diffusers-ImageDDPMPipeline #region-us \n",
"# ddpm-flowers-128-2",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/cats' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#... |
sentence-similarity | sentence-transformers |
# COS_TAPT_n_RoBERTa_STS
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Kyleiwaniec/COS_TAPT_n_RoBERTa_STS | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T12:55:41+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# COS_TAPT_n_RoBERTa_STS
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
The... | [
"# COS_TAPT_n_RoBERTa_STS\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers install... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# COS_TAPT_n_RoBERTa_STS\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like cl... |
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-turkish-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-turkish-colab", "results": []}]} | rootcodes/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-10T13:11:26+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-turkish-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: 0.4313
* Wer: 0.3336
Model description
-----------------
More informat... | [
"### 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... |
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