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text-generation | transformers |
# BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [bigscience/bloom-350m](https://huggingface.co/bigscience/bloom-350m) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## ... | {"license": "bigscience-bloom-rail-1.0", "tags": ["generated_from_trainer"], "datasets": "cmotions/Beatles_lyrics", "widget": [{"text": "Last night I couldn't sleep", "example_title": "Sleep"}, {"text": "It hasn't rained in weeks", "example_title": "Rain"}], "model-index": [{"name": "BLOOM-350m-Beatles-Lyrics-finetuned... | wvangils/BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics | null | [
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"autotrain_compatible",
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] | null | 2022-07-05T06:14:38+00:00 | [] | [] | TAGS
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| BLOOM-350m-Beatles-Lyrics-finetuned-newlyrics
=============================================
This model is a fine-tuned version of bigscience/bloom-350m on the Cmotions - Beatles lyrics dataset. It will complete an input prompt with Beatles-like text.
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* 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 #bloom #text-generation #generated_from_trainer #dataset-cmotions/Beatles_lyrics #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
fill-mask | transformers |
# HUPD DistilRoBERTa-Base Model
This HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. It was originally introduced in [this paper](TBD).
For more information about the Harvard USPTO Patent Dataset, please feel free to visit the [project website](https://patentda... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["hupd", "roberta", "distilroberta", "patents"], "datasets": ["HUPD/hupd"], "thumbnail": "url to a thumbnail used in social sharing"} | HUPD/hupd-distilroberta-base | null | [
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"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T06:41:29+00:00 | [] | [
"en"
] | TAGS
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|
# HUPD DistilRoBERTa-Base Model
This HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. It was originally introduced in this paper.
For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub r... | [
"# HUPD DistilRoBERTa-Base Model\n\nThis HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. It was originally introduced in this paper. \n\nFor more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #hupd #distilroberta #patents #en #dataset-HUPD/hupd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# HUPD DistilRoBERTa-Base Model\n\nThis HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language model... |
translation | null |
# The first testing model
| {"language": ["en"], "license": "apache-2.0", "tags": ["translation"], "datasets": ["wmt19"], "metrics": ["bleu", "sacrebleu"]} | Stero/test1 | null | [
"translation",
"en",
"dataset:wmt19",
"license:apache-2.0",
"region:us"
] | null | 2022-07-05T06:43:24+00:00 | [] | [
"en"
] | TAGS
#translation #en #dataset-wmt19 #license-apache-2.0 #region-us
|
# The first testing model
| [
"# The first testing model"
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"TAGS\n#translation #en #dataset-wmt19 #license-apache-2.0 #region-us \n",
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] |
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. -->
# hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep30
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep30", "results": []}]} | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep30 | null | [
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] | null | 2022-07-05T06:57:18+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep30
=============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.6958
* Epoch: 29
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
summarization | transformers |
# HUPD T5-Small Summarization Model
This HUPD T5-Small summarization model was fine-tuned on the HUPD dataset. It was originally introduced in [this paper](TBD).
For more information about the Harvard USPTO Patent Dataset, please feel free to visit the [project website](https://patentdataset.org/) or the [project's... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["hupd", "t5", "summarization", "conditional-generation", "patents"], "datasets": ["HUPD/hupd"]} | HUPD/hupd-t5-small | null | [
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"t5",
"text2text-generation",
"hupd",
"summarization",
"conditional-generation",
"patents",
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"dataset:HUPD/hupd",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T07:02:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #hupd #summarization #conditional-generation #patents #en #dataset-HUPD/hupd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# HUPD T5-Small Summarization Model
This HUPD T5-Small summarization model was fine-tuned on the HUPD dataset. It was originally introduced in this paper.
For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository.
### How to Use
... | [
"# HUPD T5-Small Summarization Model\n\nThis HUPD T5-Small summarization model was fine-tuned on the HUPD dataset. It was originally introduced in this paper. \n\nFor more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository.",
"### H... | [
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"# HUPD T5-Small Summarization Model\n\nThis HUPD T5-Small summarization... |
image-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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | HekmatTaherinejad/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T07:15:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0653
* Accuracy: 0.98
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: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
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-tweeteval-hate-speech
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-tweeteval-hate-speech", "results": []}]} | semy/finetuning-tweeteval-hate-speech | null | [
"transformers",
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"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T07:23:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-tweeteval-hate-speech
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8397
- Accuracy: 0.0
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
More information nee... | [
"# finetuning-tweeteval-hate-speech\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8397\n- Accuracy: 0.0\n- F1: 0.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\n... | [
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"# finetuning-tweeteval-hate-speech\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/440
This model use the following setup:
* length of chunk is 32 frames (i.e., 0.32s)
* length of right context is 8 frames (i.e., 0.08s)
| {} | Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05 | null | [
"tensorboard",
"region:us"
] | null | 2022-07-05T08:44:16+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
This model use the following setup:
* length of chunk is 32 frames (i.e., 0.32s)
* length of right context is 8 frames (i.e., 0.08s)
| [
"# Introduction\n\nSee URL\n\nThis model use the following setup:\n* length of chunk is 32 frames (i.e., 0.32s)\n* length of right context is 8 frames (i.e., 0.08s)"
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] |
fill-mask | transformers |
## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-V-large-query). For additional details, please visit:
* paper: ... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["splade", "query-expansion", "document-expansion", "bag-of-words", "passage-retrieval", "knowledge-distillation", "document encoder"], "datasets": ["ms_marco"]} | naver/efficient-splade-V-large-doc | null | [
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"knowledge-distillation",
"document encoder",
"en",
"dataset:ms_marco",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible... | null | 2022-07-05T08:45:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| Efficient SPLADE
----------------
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the doc one, please also download the query one (URL For additional details, please visit:
* paper: URL
* code: URL
If you use our checkpoint, pleas... | [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "convnext-tiny-224-finetuned-eurosat-albumentations", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "... | aihub007/convnext-tiny-224-finetuned-eurosat-albumentations | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-07-05T08:48:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #convnext #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| convnext-tiny-224-finetuned-eurosat-albumentations
==================================================
This model is a fine-tuned version of facebook/convnext-tiny-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0886
* Accuracy: 0.9804
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* l... |
summarization | transformers | # mT5-base fine-tuned for News article Summarisation ✏️🧾
[Google's mT5](https://aclanthology.org/2021.naacl-main.41/) for **summarisation** downstream task.
# Model summary
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base,... | {"language": ["da"], "tags": ["summarization"], "widget": [{"text": "De strejkende SAS-piloter melder sig nu klar til g\u00f8re en undtagelse fra strejken for at hente strandede charterg\u00e6ster hjem fra flere ferieomr\u00e5der.\nUndtagelsen skal g\u00e6lde nogle uger frem, men piloterne vil under ingen omst\u00e6ndi... | Danish-summarisation/DanSumT5-pilot | null | [
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"safetensors",
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"text2text-generation",
"summarization",
"da",
"arxiv:1804.11283",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T09:06:53+00:00 | [
"1804.11283"
] | [
"da"
] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #da #arxiv-1804.11283 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # mT5-base fine-tuned for News article Summarisation ️
Google's mT5 for summarisation downstream task.
# Model summary
This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens us... | [
"# mT5-base fine-tuned for News article Summarisation ️\n\nGoogle's mT5 for summarisation downstream task.",
"# Model summary\nThis repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to inclu... | [
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"# mT5-base fine-tuned for News article Summarisation ️\n\nGoogle's mT5 for summarisation downstream task.",
"# Model ... |
null | sklearn |
## Baseline Model trained on tips to predict sex
Metrics of the best model:
accuracy 0.647364
average_precision 0.481257
roc_auc 0.608805
recall_macro 0.588751
f1_macro 0.588435
Name: MultinomialNB(), dtype: float64
See model plot below:
<style>#sk-container-i... | {"license": "apache-2.0", "library_name": "sklearn"} | osanseviero/tips | null | [
"sklearn",
"license:apache-2.0",
"region:us"
] | null | 2022-07-05T09:07:27+00:00 | [] | [] | TAGS
#sklearn #license-apache-2.0 #region-us
|
## Baseline Model trained on tips to predict sex
Metrics of the best model:
accuracy 0.647364
average_precision 0.481257
roc_auc 0.608805
recall_macro 0.588751
f1_macro 0.588435
Name: MultinomialNB(), dtype: float64
See model plot below:
<style>#sk-container-i... | [
"## Baseline Model trained on tips to predict sex\n\nMetrics of the best model:\n\naccuracy 0.647364\n\naverage_precision 0.481257\n\nroc_auc 0.608805\n\nrecall_macro 0.588751\n\nf1_macro 0.588435\n\nName: MultinomialNB(), dtype: float64\n\n\n\nSee model plot below:\n... | [
"TAGS\n#sklearn #license-apache-2.0 #region-us \n",
"## Baseline Model trained on tips to predict sex\n\nMetrics of the best model:\n\naccuracy 0.647364\n\naverage_precision 0.481257\n\nroc_auc 0.608805\n\nrecall_macro 0.588751\n\nf1_macro 0.588435\n\nName: Multinom... |
fill-mask | transformers | ## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **query** one, please also download the **doc** one (https://huggingface.co/naver/efficient-splade-V-large-doc). For additional details, please visit:
* paper: http... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["splade", "query-expansion", "document-expansion", "bag-of-words", "passage-retrieval", "knowledge-distillation", "document encoder"], "datasets": ["ms_marco"]} | naver/efficient-splade-V-large-query | null | [
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"knowledge-distillation",
"document encoder",
"en",
"dataset:ms_marco",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible... | null | 2022-07-05T09:29:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| ## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the query one, please also download the doc one (URL For additional details, please visit:
* paper: URL
* code: URL
| | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | ... | [
"## Efficient SPLADE \nEfficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the query one, please also download the doc one (URL For additional details, please visit:\n* paper: URL\n* code: URL\n| | MRR@10 (MS MARCO dev) | R@1000 (MS MARC... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Efficient SPLADE \nEfficient S... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data2vec-text-finetuned-squad2
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebo... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "data2vec-text-finetuned-squad2", "results": []}]} | aspis/data2vec-text-finetuned-squad2 | null | [
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T09:58:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #data2vec-text #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| data2vec-text-finetuned-squad2
==============================
This model is a fine-tuned version of facebook/data2vec-text-base on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1044
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### 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 #data2vec-text #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #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\\_si... |
token-classification | transformers |
# bert-ancient-chinese-base-upos
## Model Description
This is a BERT model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [bert-ancient-chinese](https://huggingface.co/Jihuai/bert-ancient-chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (... | {"language": ["lzh"], "license": "apache-2.0", "tags": ["classical chinese", "literary chinese", "ancient chinese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u5b50\u66f0\u5b78\u800c\u6642\u7fd2\u4e4b\u4e0d\... | KoichiYasuoka/bert-ancient-chinese-base-upos | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"classical chinese",
"literary chinese",
"ancient chinese",
"pos",
"dependency-parsing",
"lzh",
"dataset:universal_dependencies",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T10:13:40+00:00 | [] | [
"lzh"
] | TAGS
#transformers #pytorch #bert #token-classification #classical chinese #literary chinese #ancient chinese #pos #dependency-parsing #lzh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-ancient-chinese-base-upos
## Model Description
This is a BERT model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from bert-ancient-chinese. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to Use
or
## See Also
esupar: Tokenizer POS-ta... | [
"# bert-ancient-chinese-base-upos",
"## Model Description\n\nThis is a BERT model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from bert-ancient-chinese. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",
"## How to Use\n\n\n\nor",
"## See Also\n\nes... | [
"TAGS\n#transformers #pytorch #bert #token-classification #classical chinese #literary chinese #ancient chinese #pos #dependency-parsing #lzh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-ancient-chinese-base-upos",
"## Model Description\... |
tabular-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624
## Validation Metrics
- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023... | {"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["abhishek/autotrain-data-iris-train", "scikit-learn/iris"], "co2_eq_emissions": 0.0006300767567816624} | abhishek/autotrain-iris-logistic-regression | null | [
"transformers",
"joblib",
"logistic_regression",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:abhishek/autotrain-data-iris-train",
"dataset:scikit-learn/iris",
"co2_eq_emissions",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-05T10:36:06+00:00 | [] | [] | TAGS
#transformers #joblib #logistic_regression #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624
## Validation Metrics
- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 9705273\n- CO2 Emissions (in grams): 0.0006300767567816624",
"## Validation Metrics\n\n- Loss: 0.15987505325856152\n- Accuracy: 0.9\n- Macro F1: 0.899749373433584\n- Micro F1: 0.9\n- Weighted F1: 0.8997493734335841\n- Macr... | [
"TAGS\n#transformers #joblib #logistic_regression #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Cl... |
fill-mask | transformers |
## K-12BERT model
K-12BERT is a model trained by performing continued pretraining on the K-12Corpus. Since, performance of BERT like models on domain adaptive tasks have shown great progress, we noticed the lack of such a model for the education domain (especially K-12 education). On that end we present K-12BERT, a BE... | {"language": "en", "license": "apache-2.0", "tags": ["education", "K-12"], "datasets": ["vasugoel/K-12Corpus"]} | vasugoel/K-12BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"education",
"K-12",
"en",
"dataset:vasugoel/K-12Corpus",
"arxiv:2205.12335",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T10:37:01+00:00 | [
"2205.12335"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #education #K-12 #en #dataset-vasugoel/K-12Corpus #arxiv-2205.12335 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## K-12BERT model
K-12BERT is a model trained by performing continued pretraining on the K-12Corpus. Since, performance of BERT like models on domain adaptive tasks have shown great progress, we noticed the lack of such a model for the education domain (especially K-12 education). On that end we present K-12BERT, a BE... | [
"## K-12BERT model\nK-12BERT is a model trained by performing continued pretraining on the K-12Corpus. Since, performance of BERT like models on domain adaptive tasks have shown great progress, we noticed the lack of such a model for the education domain (especially K-12 education). On that end we present K-12BERT,... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #education #K-12 #en #dataset-vasugoel/K-12Corpus #arxiv-2205.12335 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## K-12BERT model\nK-12BERT is a model trained by performing continued pretraining on the K-12Corpus. Since, performan... |
tabular-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705277
- CO2 Emissions (in grams): 0.15028701199056024
## Validation Metrics
- Loss: 0.15622713916762193
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.902356... | {"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["abhishek/autotrain-data-iris-train", "scikit-learn/iris"], "co2_eq_emissions": 0.15028701199056024} | abhishek/autotrain-iris-knn | null | [
"transformers",
"joblib",
"knn",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:abhishek/autotrain-data-iris-train",
"dataset:scikit-learn/iris",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T10:37:31+00:00 | [] | [] | TAGS
#transformers #joblib #knn #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705277
- CO2 Emissions (in grams): 0.15028701199056024
## Validation Metrics
- Loss: 0.15622713916762193
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.902356... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 9705277\n- CO2 Emissions (in grams): 0.15028701199056024",
"## Validation Metrics\n\n- Loss: 0.15622713916762193\n- Accuracy: 0.9\n- Macro F1: 0.899749373433584\n- Micro F1: 0.9\n- Weighted F1: 0.8997493734335841\n- Macro ... | [
"TAGS\n#transformers #joblib #knn #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 9... |
fill-mask | transformers | ## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-query). For additional details, please visit:
* paper... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["splade", "query-expansion", "document-expansion", "bag-of-words", "passage-retrieval", "knowledge-distillation", "document encoder"], "datasets": ["ms_marco"]} | naver/efficient-splade-VI-BT-large-doc | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"splade",
"query-expansion",
"document-expansion",
"bag-of-words",
"passage-retrieval",
"knowledge-distillation",
"document encoder",
"en",
"dataset:ms_marco",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible... | null | 2022-07-05T10:37:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us #has_space
| ## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the doc one, please also download the query one (URL For additional details, please visit:
* paper: URL
* code: URL
| | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | ... | [
"## Efficient SPLADE \nEfficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the doc one, please also download the query one (URL For additional details, please visit:\n* paper: URL\n* code: URL\n| | MRR@10 (MS MARCO dev) | R@1000 (MS MARC... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us #has_space \n",
"## Efficient SPLADE \n... |
tabular-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705278
- CO2 Emissions (in grams): 1.9138035947108896
## Validation Metrics
- Loss: 0.2559724063922962
- Accuracy: 0.8666666666666667
- Macro F1: 0.8666666666666668
- Micro F1: 0.8666666666666667
- Weighted F1: 0.866666666666666... | {"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["abhishek/autotrain-data-iris-train", "scikit-learn/iris"], "co2_eq_emissions": 1.9138035947108896} | abhishek/autotrain-iris-xgboost | null | [
"transformers",
"joblib",
"xgboost",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:abhishek/autotrain-data-iris-train",
"dataset:scikit-learn/iris",
"co2_eq_emissions",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-05T10:37:58+00:00 | [] | [] | TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 9705278
- CO2 Emissions (in grams): 1.9138035947108896
## Validation Metrics
- Loss: 0.2559724063922962
- Accuracy: 0.8666666666666667
- Macro F1: 0.8666666666666668
- Micro F1: 0.8666666666666667
- Weighted F1: 0.866666666666666... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 9705278\n- CO2 Emissions (in grams): 1.9138035947108896",
"## Validation Metrics\n\n- Loss: 0.2559724063922962\n- Accuracy: 0.8666666666666667\n- Macro F1: 0.8666666666666668\n- Micro F1: 0.8666666666666667\n- Weighted F1:... | [
"TAGS\n#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification... |
fill-mask | transformers | ## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **query** one, please also download the **doc** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-doc). For additional details, please visit:
* paper: ... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["splade", "query-expansion", "document-expansion", "bag-of-words", "passage-retrieval", "knowledge-distillation", "document encoder"], "datasets": ["ms_marco"]} | naver/efficient-splade-VI-BT-large-query | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"splade",
"query-expansion",
"document-expansion",
"bag-of-words",
"passage-retrieval",
"knowledge-distillation",
"document encoder",
"en",
"dataset:ms_marco",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"... | null | 2022-07-05T10:39:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us #has_space
| ## Efficient SPLADE
Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the query one, please also download the doc one (URL For additional details, please visit:
* paper: URL
* code: URL
| | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | ... | [
"## Efficient SPLADE \nEfficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the query one, please also download the doc one (URL For additional details, please visit:\n* paper: URL\n* code: URL\n| | MRR@10 (MS MARCO dev) | R@1000 (MS MARC... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #splade #query-expansion #document-expansion #bag-of-words #passage-retrieval #knowledge-distillation #document encoder #en #dataset-ms_marco #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us #has_space \n",
"## Efficient SPLADE \nEffici... |
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", "args": "PAN-X.de"}, "me... | arashba/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 | 2022-07-05T10:41:42+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: 0.1372
* F1: 0.8621
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\\_... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="infinitejoy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | infinitejoy/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-05T11:04:09+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
tabular-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977
## Validation Metrics
- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.71... | {"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["abhishek/autotrain-data-adult-train", "scikit-learn/adult-census-income"], "co2_eq_emissions": 0.12693590577861977} | abhishek/autotrain-adult-census-xgboost | null | [
"transformers",
"joblib",
"xgboost",
"autotrain",
"tabular",
"classification",
"tabular-classification",
"dataset:abhishek/autotrain-data-adult-train",
"dataset:scikit-learn/adult-census-income",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T11:06:35+00:00 | [] | [] | TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-adult-train #dataset-scikit-learn/adult-census-income #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977
## Validation Metrics
- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.71... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 9725286\n- CO2 Emissions (in grams): 0.12693590577861977",
"## Validation Metrics\n\n- Loss: 0.26716182056213406\n- Accuracy: 0.8750191923844618\n- Precision: 0.7840481565086531\n- Recall: 0.6641172721478649\n- AUC: 0.934532280... | [
"TAGS\n#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-adult-train #dataset-scikit-learn/adult-census-income #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification... |
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="bothrajat/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": "7.54 +/... | bothrajat/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-05T11:11:03+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"
] |
image-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. -->
# amyeroberts/resnet-18-finetuned-eurosat
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/r... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "amyeroberts/resnet-18-finetuned-eurosat", "results": []}]} | amyeroberts/resnet-18-finetuned-eurosat | null | [
"transformers",
"tf",
"tensorboard",
"resnet",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T11:25:12+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #resnet #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| amyeroberts/resnet-18-finetuned-eurosat
=======================================
This model is a fine-tuned version of microsoft/resnet-18 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5582
* Validation Loss: 2.1533
* Validation Accuracy: 0.2059
* Epoch: 2
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #resnet #image-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
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. -->
# hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep60
This model is a fine-tuned version of [bert-base-uncased](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep60", "results": []}]} | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep60 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T11:41:49+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep60
=============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8314
* Epoch: 59
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": []}]} | chiranthans23/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T12:00:24+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-clinc
=======================================
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: 0.7721
* Accuracy: 0.9184
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\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 #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... |
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... | micheljperez/dqn-SpaceInvadersNoFrameskip-v4-2 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T12:17:28+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... |
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-distilbert-base-uncased-5000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-distilbert-base-uncased-5000-samples", "results": []}]} | anneke/finetuning-distilbert-base-uncased-5000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T12:25:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-distilbert-base-uncased-5000-samples
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: 0.1147
- Accuracy: 0.982
- F1: 0.9904
## Model description
More information needed
## Intended uses & limitations... | [
"# finetuning-distilbert-base-uncased-5000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1147\n- Accuracy: 0.982\n- F1: 0.9904",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-distilbert-base-uncased-5000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown data... |
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": [{... | a-doering/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-05T12:26:02+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... |
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_NCC_lm-finetuned-sv-frp-classifier-3
This model is a fine-tuned version of [north/t5_small_NCC_lm](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["norwegian_parliament"], "model-index": [{"name": "t5_small_NCC_lm-finetuned-sv-frp-classifier-3", "results": []}]} | jakka/t5_small_NCC_lm-finetuned-sv-frp-classifier-3 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:norwegian_parliament",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T12:30:04+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-norwegian_parliament #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5\_small\_NCC\_lm-finetuned-sv-frp-classifier-3
================================================
This model is a fine-tuned version of north/t5\_small\_NCC\_lm on the norwegian\_parliament dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Sequence Accuracy: 0.0
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\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\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-norwegian_parliament #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n... |
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... | ramonzaca/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T12:31:59+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... |
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. -->
# sentiment-10Epochs
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unkno... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentiment-10Epochs", "results": []}]} | sepidmnorozy/sentiment-10Epochs | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T12:40:12+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sentiment-10Epochs
==================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7030
* Accuracy: 0.8603
* F1: 0.8585
* Precision: 0.8699
* Recall: 0.8473
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* e... |
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. -->
# Tinchoroman/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Tinchoroman/distilbert-base-uncased-finetuned-imdb", "results": []}]} | Tinchoroman/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T12:43:17+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Tinchoroman/distilbert-base-uncased-finetuned-imdb
==================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8509
* Validation Loss: 2.5629
* Epoch: 0
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
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... | michauhl/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T13:17:20+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| 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: 0.1891
* Accuracy: 0.9405
* F1: 0.9405
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: 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1089139622
- CO2 Emissions (in grams): 7.2566545568791945
## Validation Metrics
- Loss: 2.4398036003112793
- Rouge1: 15.4155
- Rouge2: 6.5786
- RougeL: 12.3257
- RougeLsum: 13.9424
- Gen Len: 19.0
## Usage
You can use cURL to access this mo... | {"language": "en", "tags": "autotrain", "datasets": ["tho-clare/autotrain-data-Text-Generate"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.2566545568791945} | tho-clare/autotrain-Text-Generate-1089139622 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"en",
"dataset:tho-clare/autotrain-data-Text-Generate",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T13:42:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-tho-clare/autotrain-data-Text-Generate #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1089139622
- CO2 Emissions (in grams): 7.2566545568791945
## Validation Metrics
- Loss: 2.4398036003112793
- Rouge1: 15.4155
- Rouge2: 6.5786
- RougeL: 12.3257
- RougeLsum: 13.9424
- Gen Len: 19.0
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1089139622\n- CO2 Emissions (in grams): 7.2566545568791945",
"## Validation Metrics\n\n- Loss: 2.4398036003112793\n- Rouge1: 15.4155\n- Rouge2: 6.5786\n- RougeL: 12.3257\n- RougeLsum: 13.9424\n- Gen Len: 19.0",
"## Usage\n\nYou can u... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1089139622\... |
text-classification | transformers | A base de dados utilizada para treinar o modelo cross-encoder foram duas bases públicas em português. Os corpus [ASSIN](https://huggingface.co/datasets/assin) e [ASSIN2](https://huggingface.co/datasets/assin2) (Avaliação de Similaridade Semântica e inferência textual. Resumo dos conjuntos de dados:
ASSIN - O corp... | {} | anatel/cross-encoder-pt-sentence-similarity | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T13:59:46+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| A base de dados utilizada para treinar o modelo cross-encoder foram duas bases públicas em português. Os corpus ASSIN e ASSIN2 (Avaliação de Similaridade Semântica e inferência textual. Resumo dos conjuntos de dados:
ASSIN - O corpus contém pares de frases extraídas de notícias escritas em português europeu (PE) ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | spacy | GermanBERT-based model of the GERNERMED++ German NER model for medical entities.
| Feature | Description |
| --- | --- |
| **Name** | `de_GERNERMEDpp_GottBERT` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vect... | {"language": ["de"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Zur weiteren Bek\u00e4mpfung der Symptomatik wird die Einnahme von t\u00e4glich 100mg Cortison als Tablette empfohlen."}], "pipeline_tag": "token-classification", "pretty_name": "GERNERMED++ (GottBERT-based)"} | jfrei/de_GERNERMEDpp_GottBERT | null | [
"spacy",
"token-classification",
"de",
"model-index",
"region:us"
] | null | 2022-07-05T14:02:17+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #model-index #region-us
| GermanBERT-based model of the GERNERMED++ German NER model for medical entities.
### Label Scheme
View label scheme (6 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
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"TAGS\n#spacy #token-classification #de #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] |
token-classification | spacy | Slim model of the GERNERMED++ German NER model for medical entities.
| Feature | Description |
| --- | --- |
| **Name** | `de_GERNERMEDpp_Slim` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique... | {"language": ["de"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Zur weiteren Bek\u00e4mpfung der Symptomatik wird die Einnahme von t\u00e4glich 100mg Cortison als Tablette empfohlen."}], "pipeline_tag": "token-classification", "pretty_name": "GERNERMED++ (SpaCy DE Slim-based)"} | jfrei/de_GERNERMEDpp_Slim | null | [
"spacy",
"token-classification",
"de",
"model-index",
"region:us"
] | null | 2022-07-05T14:08:59+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #model-index #region-us
| Slim model of the GERNERMED++ German NER model for medical entities.
### Label Scheme
View label scheme (6 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] |
token-classification | spacy | GottBERT-based model of the GERNERMED++ German NER model for medical entities.
| Feature | Description |
| --- | --- |
| **Name** | `de_GERNERMEDpp_GermanBERT` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vect... | {"language": ["de"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Zur weiteren Bek\u00e4mpfung der Symptomatik wird die Einnahme von t\u00e4glich 100mg Cortison als Tablette empfohlen."}], "pipeline_tag": "token-classification", "pretty_name": "GERNERMED++ (GermanBERT-based)"} | jfrei/de_GERNERMEDpp_GermanBERT | null | [
"spacy",
"token-classification",
"de",
"model-index",
"region:us"
] | null | 2022-07-05T14:09:48+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #model-index #region-us
| GottBERT-based model of the GERNERMED++ German NER model for medical entities.
### Label Scheme
View label scheme (6 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 1 components)",
"### Accuracy"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | akhisreelibra/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T14:30:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3810
* Rouge1: 5.5031
* Rouge2: 1.0338
* Rougel: 5.5913
* Rougelsum: 5.5823
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
null | null | Test: https://googgle.com | {} | coyotte508/__repo_type__ | null | [
"region:us"
] | null | 2022-07-05T14:34:41+00:00 | [] | [] | TAGS
#region-us
| Test: URL | [] | [
"TAGS\n#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. -->
# 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": []}]} | Eleven/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 | 2022-07-05T14:37:17+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: 0.1644
* F1: 0.8617
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*... |
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-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | Krisna/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T14:42:13+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-3000-samples
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: 0.3366
- Accuracy: 0.86
- F1: 0.8636
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3366\n- Accuracy: 0.86\n- F1: 0.8636",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"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-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt... |
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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | jdang/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T15:15:10+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0562
* Accuracy: 0.9352
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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
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-fr
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-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | Eleven/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T15:20:12+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
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: 0.2867
* F1: 0.8355
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 #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\\_rate: 5e-05\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. -->
# xlm-roberta-base-finetuned-panx-it
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-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | Eleven/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T15:37:09+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
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: 0.2421
* F1: 0.8248
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 #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\\_rate: 5e-05\n... |
table-question-answering | transformers |
# Model description
This is an [tapas-base](https://huggingface.co/google/tapas-base) model, trained on the lookup queries of [wikisql](https://huggingface.co/datasets/wikisql) dataset. It was trained to take tables and questions as input to extract answers from the table.
# Overview
*Language model*: tapas-base \
... | {"license": "apache-2.0"} | PrimeQA/tapas-based-tableqa-wikisql-lookup | null | [
"transformers",
"pytorch",
"tapas",
"table-question-answering",
"arxiv:2004.02349",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T15:45:00+00:00 | [
"2004.02349"
] | [] | TAGS
#transformers #pytorch #tapas #table-question-answering #arxiv-2004.02349 #license-apache-2.0 #endpoints_compatible #region-us
|
# Model description
This is an tapas-base model, trained on the lookup queries of wikisql dataset. It was trained to take tables and questions as input to extract answers from the table.
# Overview
*Language model*: tapas-base \
*Language*: English\
*Task*: Table Question Answering \
*Data*: WikiSQL
# Intented use... | [
"# Model description\n\nThis is an tapas-base model, trained on the lookup queries of wikisql dataset. It was trained to take tables and questions as input to extract answers from the table.",
"# Overview\n\n*Language model*: tapas-base \\\n*Language*: English\\\n*Task*: Table Question Answering \\\n*Data*: WikiS... | [
"TAGS\n#transformers #pytorch #tapas #table-question-answering #arxiv-2004.02349 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model description\n\nThis is an tapas-base model, trained on the lookup queries of wikisql dataset. It was trained to take tables and questions as input to extract answers ... |
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"]} | a-doering/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-05T15:49:02+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... |
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-en
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-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | Eleven/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T15:54:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
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: 0.3921
* F1: 0.6922
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 #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\\_rate: 5e-05\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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | Eleven/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T16:10:07+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-all
===================================
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: 0.1752
* F1: 0.8557
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 #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*... |
tabular-classification | sklearn |
## Baseline Model trained on tipsuhtxfu to apply classification on sex
**Metrics of the best model:**
accuracy 0.647364
average_precision 0.507660
roc_auc 0.625546
recall_macro 0.589832
f1_macro 0.585292
Name: MultinomialNB(), dtype: float64
**See model plot be... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | osanseviero/tipsuhtxfu-sex-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-05T16:18:04+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #has_space #region-us
|
## Baseline Model trained on tipsuhtxfu to apply classification on sex
Metrics of the best model:
accuracy 0.647364
average_precision 0.507660
roc_auc 0.625546
recall_macro 0.589832
f1_macro 0.585292
Name: MultinomialNB(), dtype: float64
See model plot below:
... | [
"## Baseline Model trained on tipsuhtxfu to apply classification on sex\n\nMetrics of the best model:\n\naccuracy 0.647364\n\naverage_precision 0.507660\n\nroc_auc 0.625546\n\nrecall_macro 0.589832\n\nf1_macro 0.585292\n\nName: MultinomialNB(), dtype: float64\n\n\n\nS... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #has_space #region-us \n",
"## Baseline Model trained on tipsuhtxfu to apply classification on sex\n\nMetrics of the best model:\n\naccuracy 0.647364\n\naverage_precision 0.507660\n\nroc_auc 0.625546\n\nrecal... |
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. -->
# LogClassification
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "LogClassification", "results": []}]} | SushantGautam/LogClassification | null | [
"transformers",
"pytorch",
"canine",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T16:41:50+00:00 | [] | [] | TAGS
#transformers #pytorch #canine #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# LogClassification
This model is a fine-tuned version of google/canine-c on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The... | [
"# LogClassification\n\nThis model is a fine-tuned version of google/canine-c on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"###... | [
"TAGS\n#transformers #pytorch #canine #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# LogClassification\n\nThis model is a fine-tuned version of google/canine-c on an unknown dataset.",
"## Model description\n\nMore information need... |
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-catpole-01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"... | pm390/Reinforce-catpole-01 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-05T16:49:17+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... |
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... | coledie/reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-05T17:04:20+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 ... |
text-generation | transformers | ## Model description
GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.
This is only the first version of the model; over time model will be improved using a more extensive dataset and better data preparation.
## Training data
This model was pre-trained with 180MB of... | {"language": ["lt"], "license": "apache-2.0", "tags": ["text-generation"], "datasets": ["wikipedia"], "widget": [{"text": "Lietuva yra viena "}]} | DeividasM/gpt2_lithuanian_small | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"lt",
"dataset:wikipedia",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T17:06:00+00:00 | [] | [
"lt"
] | TAGS
#transformers #tf #gpt2 #text-generation #lt #dataset-wikipedia #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
| ## Model description
!LT
GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.
This is only the first version of the model; over time model will be improved using a more extensive dataset and better data preparation.
## Training data
This model was pre-trained with 180MB of Lithuania... | [
"## Model description\n\n!LT\n\nGPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.\n\nThis is only the first version of the model; over time model will be improved using a more extensive dataset and better data preparation.",
"## Training data\nThis model was pre-trained with 18... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #lt #dataset-wikipedia #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n",
"## Model description\n\n!LT\n\nGPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.\n\nThis is only the first version of the... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="infinitejoy/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-slippery", "type": "FrozenL... | infinitejoy/q-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4-slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-07-05T17:19:09+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
tabular-classification | sklearn |
## Baseline Model trained on titanic_traink4m62li8 to apply classification on survived
**Metrics of the best model:**
accuracy 0.975294
average_precision 0.983664
roc_auc 0.987422
recall_macro 0.971786
f1_macro 0.973370
Name: MultinomialNB(), dtype: float64
**S... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | maderix/titanic_traink4m62li8-survived-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] | null | 2022-07-05T17:29:44+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us
|
## Baseline Model trained on titanic_traink4m62li8 to apply classification on survived
Metrics of the best model:
accuracy 0.975294
average_precision 0.983664
roc_auc 0.987422
recall_macro 0.971786
f1_macro 0.973370
Name: MultinomialNB(), dtype: float64
See mod... | [
"## Baseline Model trained on titanic_traink4m62li8 to apply classification on survived\n\nMetrics of the best model:\n\naccuracy 0.975294\n\naverage_precision 0.983664\n\nroc_auc 0.987422\n\nrecall_macro 0.971786\n\nf1_macro 0.973370\n\nName: MultinomialNB(), dtype: ... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us \n",
"## Baseline Model trained on titanic_traink4m62li8 to apply classification on survived\n\nMetrics of the best model:\n\naccuracy 0.975294\n\naverage_precision 0.983664\n\nroc_auc 0.987422\n\n... |
null | null |
# SOTA
SOTA (short for Sign Of The Apocalypse) is a model pretrained on all atoms in the observable universe. It achieves state-of-the-art results on every task known to humans, including those in future generations. It was introduced in the paper [_SOTA is All You Need_](https://twitter.com/wellingmax/status/1542384... | {"license": "wtfpl"} | lewtun/sota | null | [
"license:wtfpl",
"region:us"
] | null | 2022-07-05T17:50:24+00:00 | [] | [] | TAGS
#license-wtfpl #region-us
|
# SOTA
SOTA (short for Sign Of The Apocalypse) is a model pretrained on all atoms in the observable universe. It achieves state-of-the-art results on every task known to humans, including those in future generations. It was introduced in the paper _SOTA is All You Need_ and first released via Twitter.
Disclaimer: th... | [
"# SOTA\n\nSOTA (short for Sign Of The Apocalypse) is a model pretrained on all atoms in the observable universe. It achieves state-of-the-art results on every task known to humans, including those in future generations. It was introduced in the paper _SOTA is All You Need_ and first released via Twitter.\n\nDiscla... | [
"TAGS\n#license-wtfpl #region-us \n",
"# SOTA\n\nSOTA (short for Sign Of The Apocalypse) is a model pretrained on all atoms in the observable universe. It achieves state-of-the-art results on every task known to humans, including those in future generations. It was introduced in the paper _SOTA is All You Need_ a... |
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. -->
# xlm-roberta-base-finetuned-misogyny-sexism
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-r... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "xlm-roberta-base-finetuned-misogyny-sexism", "results": []}]} | annahaz/xlm-roberta-base-finetuned-misogyny-sexism | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T18:00:29+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-misogyny-sexism
==========================================
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: 0.9064
* Accuracy: 0.8334
* F1: 0.3322
* Precision: 0.2498
* Recall: 0.4961
* Mae: 0.1666... | [
"### 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 #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* ... |
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... | btsas/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T18:05:43+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... |
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-01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter... | pm390/Reinforce-pixelcopter-01 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-05T18:07:59+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 ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | osanseviero/ppo-LunarLander-v4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T18:12:02+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
## GPT2 French base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/fr](https://www.tensorflow.org/datasets/catalog/wiki40b#wiki... | {"language": "fr", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "widget": [{"text": "Je vais \u00e0 la gare, et"}, {"text": "J'aime le caf\u00e9, donc"}, {"text": "Nous avons parl\u00e9"}, {"text": "Je m'appelle"}]} | ClassCat/gpt2-base-french | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"fr",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T18:28:03+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #fr #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## GPT2 French base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses GPT2 base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* wiki40b/fr (French Wikipedia)
* Subset of CC-100/fr : Monolingual Dat... | [
"## GPT2 French base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses GPT2 base setttings except vocabulary size.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* wiki40b/fr (French Wikipedia)\n* Subset o... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #fr #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## GPT2 French base model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | BigTimeCoderSean/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T18:29:07+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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. -->
# hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep100
This model is a fine-tuned version of [bert-base-uncased](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep100", "results": []}]} | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep100 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T18:36:06+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep100
==============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7195
* Epoch: 99
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-0... |
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. -->
# twitter-roberta-base-dec2021-CoNLL
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggi... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter-roberta-base-dec2021-CoNLL", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003"... | emilys/twitter-roberta-base-dec2021-CoNLL | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T18:46:48+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-dec2021-CoNLL
==================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0412
* Precision: 0.9553
* Recall: 0.9628
* F1: 0.9590
* Accuracy: 0.9927
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 1024\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",
"### Tra... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batc... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-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": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pong-01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{"typ... | pm390/Reinforce-pong-01 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-05T18:49:16+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
image-segmentation | 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. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-2", "results": []}]} | userGagan/segformer-b0-finetuned-segments-sidewalk-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T19:02:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-sidewalk-2
==========================================
This model is a fine-tuned version of nvidia/mit-b0 on the userGagan/ResizedSample dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3429
* Mean Iou: 0.8143
* Mean Accuracy: 0.9007
* Overall Accuracy: 0.9... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\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. -->
# deberta-v3-xsmall-finetuned-review_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggin... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-xsmall-finetuned-review_classifier", "results": []}]} | domenicrosati/deberta-v3-xsmall-finetuned-review_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T19:16:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-xsmall-finetuned-review\_classifier
==============================================
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1441
* Accuracy: 0.9513
* F1: 0.7458
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
"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: 4.5e-05\n* train\\_batch\... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/990605878993793024/7uuCR... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/donaldtusk/1661948958135/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/donaldtusk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-05T19:21:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Donald Tusk
@donaldtusk
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | justinwilloughby/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T19:44:15+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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", "args": "PAN-X.de"}, "me... | venturaville/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 | 2022-07-05T20:21:27+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: 0.1367
* F1: 0.8633
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\\_... |
fill-mask | transformers |
# deberta-large-japanese-wikipedia
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 632 hours 19 minutes for training. You can fine-tune `deberta-large-japanese-wikipedia` for downstream tasks, such as [POS-tagging](https://huggingface.co/K... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-large-japanese-wikipedia | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"wikipedia",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T21:01:16+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-large-japanese-wikipedia
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 632 hours 19 minutes for training. You can fine-tune 'deberta-large-japanese-wikipedia' for downstream tasks, such as POS-tagging, dependency-parsing, and s... | [
"# deberta-large-japanese-wikipedia",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 632 hours 19 minutes for training. You can fine-tune 'deberta-large-japanese-wikipedia' for downstream tasks, such as POS-tagging, dependency-pars... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #wikipedia #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-large-japanese-wikipedia",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. ... |
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. -->
# twitter-roberta-base-dec2021-WNUT
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggin... | {"tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter-roberta-base-dec2021-WNUT", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "type": "wnut_17", "args... | emilys/twitter-roberta-base-dec2021-WNUT | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-05T21:21:52+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-dec2021-WNUT
=================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2152
* Precision: 0.7112
* Recall: 0.6244
* F1: 0.6650
* Accuracy: 0.9643
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 1024\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",
"### Tra... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-wnut_17 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\... |
image-classification | timm |
# test-hf-hub-modelcards-compatibility
## Model description
Some really helpful description...
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training d... | {"language": "en", "license": "mit", "library_name": "timm", "tags": ["image-classification", "resnet"], "datasets": "beans", "metrics": ["accuracy", "f1"]} | nateraw/test-hf-hub-modelcards-compatibility | null | [
"timm",
"image-classification",
"resnet",
"en",
"dataset:beans",
"license:mit",
"region:us"
] | null | 2022-07-05T22:12:37+00:00 | [] | [
"en"
] | TAGS
#timm #image-classification #resnet #en #dataset-beans #license-mit #region-us
|
# test-hf-hub-modelcards-compatibility
## Model description
Some really helpful description...
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initiali... | [
"# test-hf-hub-modelcards-compatibility",
"## Model description\n\nSome really helpful description...",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to trai... | [
"TAGS\n#timm #image-classification #resnet #en #dataset-beans #license-mit #region-us \n",
"# test-hf-hub-modelcards-compatibility",
"## Model description\n\nSome really helpful description...",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent i... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Varnez/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-05T23:18:08+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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-xsmall-with-biblio-context-finetuned-review_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-xs... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier", "results": []}]} | domenicrosati/deberta-v3-xsmall-with-biblio-context-finetuned-review_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T00:12:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #endpoints_compatible #region-us
| deberta-v3-xsmall-with-biblio-context-finetuned-review\_classifier
==================================================================
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0979
* Accuracy: 0.9682
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ste... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 12\n* eval\\_b... |
image-segmentation | transformers |
# Face Parsing
[Semantic segmentation](https://huggingface.co/docs/transformers/tasks/semantic_segmentation) model fine-tuned from [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) with [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ) for face parsing. For a... | {"language": "en", "library_name": "transformers", "tags": ["vision", "image-segmentation", "nvidia/mit-b5", "transformers.js", "onnx"], "datasets": ["celebamaskhq"]} | jonathandinu/face-parsing | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"nvidia/mit-b5",
"transformers.js",
"en",
"dataset:celebamaskhq",
"arxiv:2105.15203",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-06T00:22:42+00:00 | [
"2105.15203"
] | [
"en"
] | TAGS
#transformers #pytorch #onnx #safetensors #segformer #vision #image-segmentation #nvidia/mit-b5 #transformers.js #en #dataset-celebamaskhq #arxiv-2105.15203 #endpoints_compatible #has_space #region-us
| Face Parsing
============
!example image and output
Semantic segmentation model fine-tuned from nvidia/mit-b5 with CelebAMask-HQ for face parsing. For additional options, see the Transformers Segformer docs.
>
> ONNX model for web inference contributed by Xenova.
>
>
>
Usage in Python
---------------
Exh... | [
"### URL\n\n\nSince URL uses an animation loop abstraction, we need to take care loading the model and making predictions.\n\n\nfull URL example",
"### Model Description\n\n\n* Developed by: Jonathan Dinu\n* Model type: Transformer-based semantic segmentation image model\n* License: non-commercial research and ed... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #segformer #vision #image-segmentation #nvidia/mit-b5 #transformers.js #en #dataset-celebamaskhq #arxiv-2105.15203 #endpoints_compatible #has_space #region-us \n",
"### URL\n\n\nSince URL uses an animation loop abstraction, we need to take care loading the model an... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an u... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-prop-16-train-set", "results": []}]} | ultra-coder54732/roberta-base-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T00:36:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-prop-16-train-set
This model is a fine-tuned version of roberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore... |
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-end2end-questions-generation-cv-squadV2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-end2end-questions-generation-cv-squadV2", "results": []}]} | wiselinjayajos/t5-end2end-questions-generation-cv-squadV2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T01:47:35+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-end2end-questions-generation-cv-squadV2
==========================================
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8541
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: ... |
token-classification | transformers |
# deberta-large-japanese-wikipedia-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-wikipedia). Every long-unit... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u306... | KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"wikipedia",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T02:15:12+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-large-japanese-wikipedia-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-large-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
## How to ... | [
"# deberta-large-japanese-wikipedia-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-large-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.",... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-large-japanese-wikipedia-luw-upos",
"## Model Description\n\nThis is a De... |
question-answering | transformers |
# deberta-large-japanese-wikipedia-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-la... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b6... | KoichiYasuoka/deberta-large-japanese-wikipedia-ud-head | null | [
"transformers",
"pytorch",
"deberta-v2",
"question-answering",
"japanese",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T02:51:14+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# deberta-large-japanese-wikipedia-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-large-japanese-wikipedia and UD_Japanese-GSDLUW. Use [MASK] inside 'contex... | [
"# deberta-large-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from deberta-large-japanese-wikipedia and UD_Japanese-GSDLUW. Use [MASK] insi... | [
"TAGS\n#transformers #pytorch #deberta-v2 #question-answering #japanese #wikipedia #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# deberta-large-japanese-wikipedia-ud-head",
"## Model Description\n\nThis is a DeBERTa(V2) model pretrained on ... |
text2text-generation | transformers | # CodeT5 (large-size model 770M)
## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) by Yue Wang, Weishi Wang, Shafiq Joty, ... | {"license": "bsd-3-clause"} | Salesforce/codet5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2109.00859",
"arxiv:2207.01780",
"arxiv:1909.09436",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T02:56:45+00:00 | [
"2109.00859",
"2207.01780",
"1909.09436"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2109.00859 #arxiv-2207.01780 #arxiv-1909.09436 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # CodeT5 (large-size model 770M)
## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi.
The checkpoint inc... | [
"# CodeT5 (large-size model 770M)",
"## Model description\n\nCodeT5 is a family of encoder-decoder language models for code from the paper: CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi.\n\nThe c... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2109.00859 #arxiv-2207.01780 #arxiv-1909.09436 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# CodeT5 (large-size model 770M)",
"## Model description\n\nCodeT5 is a family of... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1093539673
- CO2 Emissions (in grams): 7.663051290039914
## Validation Metrics
- Loss: 0.34404119849205017
- Accuracy: 0.8843120070113936
- Macro F1: 0.8771237753798016
- Micro F1: 0.8843120070113936
- Weighted F1: 0.884349891428... | {"language": "bn", "tags": "autotrain", "datasets": ["dee4hf/autotrain-data-deephate2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.663051290039914} | dee4hf/autotrain-deephate2-1093539673 | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain",
"bn",
"dataset:dee4hf/autotrain-data-deephate2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T03:25:25+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #albert #text-classification #autotrain #bn #dataset-dee4hf/autotrain-data-deephate2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1093539673
- CO2 Emissions (in grams): 7.663051290039914
## Validation Metrics
- Loss: 0.34404119849205017
- Accuracy: 0.8843120070113936
- Macro F1: 0.8771237753798016
- Micro F1: 0.8843120070113936
- Weighted F1: 0.884349891428... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1093539673\n- CO2 Emissions (in grams): 7.663051290039914",
"## Validation Metrics\n\n- Loss: 0.34404119849205017\n- Accuracy: 0.8843120070113936\n- Macro F1: 0.8771237753798016\n- Micro F1: 0.8843120070113936\n- Weighted ... | [
"TAGS\n#transformers #pytorch #albert #text-classification #autotrain #bn #dataset-dee4hf/autotrain-data-deephate2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1093539673\n- CO2 Emissions (... |
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"]} | vebie91/resnet18-my-umamusume | null | [
"fastai",
"region:us"
] | null | 2022-07-06T03:45:34+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... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | go2k/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-06T05:26:22+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
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"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | # CodeT5 (large-size model pretrained with NTP objective on Python)
## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) by Y... | {"license": "bsd-3-clause"} | Salesforce/codet5-large-ntp-py | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2109.00859",
"arxiv:2207.01780",
"arxiv:1909.09436",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-06T05:31:57+00:00 | [
"2109.00859",
"2207.01780",
"1909.09436"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2109.00859 #arxiv-2207.01780 #arxiv-1909.09436 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # CodeT5 (large-size model pretrained with NTP objective on Python)
## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, and S... | [
"# CodeT5 (large-size model pretrained with NTP objective on Python)",
"## Model description\n\nCodeT5 is a family of encoder-decoder language models for code from the paper: CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2109.00859 #arxiv-2207.01780 #arxiv-1909.09436 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# CodeT5 (large-size model pretrained with NTP objective on Python)",
"## Model d... |
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", "args": "PAN-X.de"}, "me... | ArneD/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 | 2022-07-06T05:47:26+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: 0.1372
* F1: 0.8621
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\\_... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/aishell2_transducer`
This model was trained by jctian98 using aishell2 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 40c5f6919244c2ec8eac14b9011854dd02511a04
pip install -e .
cd egs2/aishell2/asr1
./run.sh --s... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell2"]} | espnet/aishell2_transducer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell2",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-06T05:55:04+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell2 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/aishell2\_transducer'
This model was trained by jctian98 using aishell2 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Jul 5 22:02:55 CST 2022'
* python version: '3.8.13 (default, Mar 28 2022, 11:38:47)... | [
"### 'espnet/aishell2\\_transducer'\n\n\nThis model was trained by jctian98 using aishell2 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Jul 5 22:02:55 CST 2022'\n* python version: '3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]'\... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell2 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/aishell2\\_transducer'\n\n\nThis model was trained by jctian98 using aishell2 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Shunichiro/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T05:58:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.0244
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: 60",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
question-answering | transformers |
## MODEL DESCRIPTION
huBERT base model (cased) fine-tuned on SQuADv2 (NEW!)
- huBert model + Tokenizer: https://huggingface.co/SZTAKI-HLT/hubert-base-cc
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)
<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000... | {"language": "hu", "tags": ["question-answering", "bert"], "widget": [{"text": "Melyik foly\u00f3 szeli kett\u00e9 Budapestet?", "context": "Magyarorsz\u00e1g f\u0151v\u00e1ros\u00e1t, Budapestet a Duna foly\u00f3 szeli kett\u00e9. A XIX. sz\u00e1zadban \u00e9p\u00fclt L\u00e1nch\u00edd a dimbes-dombos budai oldalt k\u... | mcsabai/huBert-fine-tuned-hungarian-squadv2 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"hu",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T06:34:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us
|
## MODEL DESCRIPTION
huBERT base model (cased) fine-tuned on SQuADv2 (NEW!)
- huBert model + Tokenizer: URL
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)
<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially... | [
"## MODEL DESCRIPTION\n\nhuBERT base model (cased) fine-tuned on SQuADv2 (NEW!) \n\n- huBert model + Tokenizer: URL\n- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)\n\n<p> <i> \"SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written a... | [
"TAGS\n#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us \n",
"## MODEL DESCRIPTION\n\nhuBERT base model (cased) fine-tuned on SQuADv2 (NEW!) \n\n- huBert model + Tokenizer: URL\n- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)\n\n<p> <i>... |
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. -->
# token_fine_tunned_flipkart
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "token_fine_tunned_flipkart", "results": []}]} | vinayak361/token_fine_tunned_flipkart | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T06:42:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| token\_fine\_tunned\_flipkart
=============================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0992
* Precision: 0.9526
* Recall: 0.9669
* F1: 0.9597
* Accuracy: 0.9730
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### 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\\_... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | messham/ppo-LunarLander-v2_1pt5m | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-06T07:33:19+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | null | valkiry robot
desert technology
| {} | laurian/pouet | null | [
"region:us"
] | null | 2022-07-06T07:42:05+00:00 | [] | [] | TAGS
#region-us
| valkiry robot
desert technology
| [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/440
This model use the following setup:
* length of chunk is 64 frames (i.e., 0.64s)
* length of right context is 16 frames (i.e., 0.16s)
| {} | Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-larger-latency-2022-07-06 | null | [
"tensorboard",
"region:us"
] | null | 2022-07-06T07:44:45+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
This model use the following setup:
* length of chunk is 64 frames (i.e., 0.64s)
* length of right context is 16 frames (i.e., 0.16s)
| [
"# Introduction\n\nSee URL\n\nThis model use the following setup:\n* length of chunk is 64 frames (i.e., 0.64s)\n* length of right context is 16 frames (i.e., 0.16s)"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL\n\nThis model use the following setup:\n* length of chunk is 64 frames (i.e., 0.64s)\n* length of right context is 16 frames (i.e., 0.16s)"
] |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP | {"license": "afl-3.0"} | sumitrsch/xlm_R_large_multiconer22_hi | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-06T08:04:53+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. URL | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | # Model Overview
A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation).
## Workflow
... | {"tags": ["monai"]} | dnouri/brats_mri_segmentation | null | [
"monai",
"arxiv:1810.11654",
"region:us"
] | null | 2022-07-06T08:13:12+00:00 | [
"1810.11654"
] | [] | TAGS
#monai #arxiv-1810.11654 #region-us
| # Model Overview
A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from clara_pt_brain_mri_segmentation.
## Workflow
The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "en... | [
"# Model Overview\nA pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from clara_pt_brain_mri_segmentation.",
"## Workflow\n\nThe model is trained to segment 3 nested subregions of primary brain tumors (gliom... | [
"TAGS\n#monai #arxiv-1810.11654 #region-us \n",
"# Model Overview\nA pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from clara_pt_brain_mri_segmentation.",
"## Workflow\n\nThe model is trained to segment ... |
text-generation | null |
# RWKV-3 169M
## Model Description
RWKV-3 169M is a L12-D768 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it.
ctx_len = 768
n_layer = 12
n_embd = 768
Final checkpoin... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["The Pile"]} | BlinkDL/rwkv-3-pile-169m | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-06T08:16:20+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us
|
# RWKV-3 169M
## Model Description
RWKV-3 169M is a L12-D768 causal language model trained on the Pile. See URL for details.
At this moment you have to use my Github code (URL to run it.
ctx_len = 768
n_layer = 12
n_embd = 768
Final checkpoint:
URL : Trained on the Pile for 328B tokens.
* Pile loss 2.5596
* LAMBA... | [
"# RWKV-3 169M",
"## Model Description\n\nRWKV-3 169M is a L12-D768 causal language model trained on the Pile. See URL for details.\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = 768\nn_layer = 12\nn_embd = 768\n\nFinal checkpoint:\nURL : Trained on the Pile for 328B tokens.\n* Pile... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us \n",
"# RWKV-3 169M",
"## Model Description\n\nRWKV-3 169M is a L12-D768 causal language model trained on the Pile. See URL for details.\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = 7... |
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