pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-financial-phrasebank-allagree1
This model is a fine-tuned version of [roberta-large](https://huggingface.co/robert... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["financial_phrasebank"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-large-financial-phrasebank-allagree1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phraseb... | Farshid/roberta-large-financial-phrasebank-allagree1 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:financial_phrasebank",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T18:16:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-financial-phrasebank-allagree1
============================================
This model is a fine-tuned version of roberta-large on the financial\_phrasebank dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1417
* Accuracy: 0.9735
* F1: 0.9736
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-financial_phrasebank #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* le... |
null | null | A simple single label classification model, ResNet18, to predict whether the provided image is a cat or a dog. The model was created in Fast.ai
and exported to ONNX using PyTorch's ONNX export capabilities.
The source dataset is the OXFORD-IIIT PET. Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawaha... | {"license": "mit"} | ScottMueller/Cats_v_Dogs.ONNX | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2022-08-08T18:54:50+00:00 | [] | [] | TAGS
#onnx #license-mit #region-us
| A simple single label classification model, ResNet18, to predict whether the provided image is a cat or a dog. The model was created in URL
and exported to ONNX using PyTorch's ONNX export capabilities.
The source dataset is the OXFORD-IIIT PET. Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawahar
We... | [] | [
"TAGS\n#onnx #license-mit #region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | DavidNovikov/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-08T19:22:07+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
null | null | A simple single label classification model, ResNet18, to predict the cat or dog breed from the provided image. The model was created in Fast.ai
and exported to ONNX using PyTorch's ONNX export capabilities.
The source dataset is the OXFORD-IIIT PET. Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawaha... | {"license": "mit"} | ScottMueller/Cat_Dog_Breeds.ONNX | null | [
"onnx",
"license:mit",
"region:us"
] | null | 2022-08-08T19:28:18+00:00 | [] | [] | TAGS
#onnx #license-mit #region-us
| A simple single label classification model, ResNet18, to predict the cat or dog breed from the provided image. The model was created in URL
and exported to ONNX using PyTorch's ONNX export capabilities.
The source dataset is the OXFORD-IIIT PET. Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawahar
We... | [] | [
"TAGS\n#onnx #license-mit #region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relb... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"... | research-backup/roberta-large-conceptnet-average-no-mask-prompt-b-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T19:45:41+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset... | [
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Questi... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning... |
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. -->
# 3-way-detection-prop-16
This model is a fine-tuned version of [ultra-coder54732/3-way-detection-prop-16](https://huggingface.co/... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "3-way-detection-prop-16", "results": []}]} | ultra-coder54732/3-way-detection-prop-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T19:58:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# 3-way-detection-prop-16
This model is a fine-tuned version of ultra-coder54732/3-way-detection-prop-16 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# 3-way-detection-prop-16\n\nThis model is a fine-tuned version of ultra-coder54732/3-way-detection-prop-16 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",
... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# 3-way-detection-prop-16\n\nThis model is a fine-tuned version of ultra-coder54732/3-way-detection-prop-16 on an unknown dataset.",
"## Mode... |
text-generation | transformers |
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... | {"language": "en", "license": "mit", "tags": ["exbert"]} | model-attribution-challenge/gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T20:30:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #tflite #rust #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #pytorch #tf #jax #tflite #rust #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation re... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-v8-e1
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-v8-e1", "results": []}]} | theojolliffe/bart-paraphrase-v8-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T20:40:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v8-e1
=====================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1597
* Rouge1: 73.0494
* Rouge2: 70.2389
* Rougel: 72.0086
* Rougelsum: 72.1
* Gen Len: 19.7365
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-dril-dril9999-dril_gpt2-gptmicrofic-tanakhbot/1659995519837/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-dril-dril9999-dril_gpt2-gptmicrofic-tanakhbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T20:48:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & tanakhbot & GPT2-Microfic & MORTIMUS COWBOY: The Bastard of Diapers & wint & wint but Al
@apesahoy-dril-dril9999-dril\_gpt2-gptmicrofic-tanakhbot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
Disclaimer... | {"language": "en", "license": "mit", "datasets": ["bookcorpus", "wikipedia"]} | model-attribution-challenge/xlnet-base-cased | null | [
"transformers",
"pytorch",
"tf",
"rust",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T20:53:14+00:00 | [
"1906.08237"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #rust #xlnet #text-generation #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1906.08237 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al. and first released in this repository.
Disclaimer: The team releasing XLNet did not write a model card for this model so thi... | [
"# XLNet (base-sized model) \n\nXLNet model pre-trained on English language. It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al. and first released in this repository. \n\nDisclaimer: The team releasing XLNet did not write a model card for this mode... | [
"TAGS\n#transformers #pytorch #tf #rust #xlnet #text-generation #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1906.08237 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLNet (base-sized model) \n\nXLNet model pre-trained on English language. It was introduced in the paper XLNet: G... |
null | null | Hugging Face's logo
---
tags:
- object-detection
- vision
library_name: faster_rcnn
datasets:
- coco
---
# Faster R-CNN
## Model desription
This model is an enhanced version of the [Fast R-CNN model](https://arxiv.org/pdf/1504.08083.pdf). Due to the computation bottleneck posed by Fast-RCNN that saw the innovation ... | {} | blesot/Faster-R-CNN-Object-detection | null | [
"arxiv:1504.08083",
"arxiv:1703.06870",
"has_space",
"region:us"
] | null | 2022-08-08T21:39:49+00:00 | [
"1504.08083",
"1703.06870"
] | [] | TAGS
#arxiv-1504.08083 #arxiv-1703.06870 #has_space #region-us
| Hugging Face's logo
---
tags:
- object-detection
- vision
library_name: faster_rcnn
datasets:
- coco
---
# Faster R-CNN
## Model desription
This model is an enhanced version of the Fast R-CNN model. Due to the computation bottleneck posed by Fast-RCNN that saw the innovation of Region of Pooling. Faster-RCNN introd... | [
"# Faster R-CNN",
"## Model desription\n\nThis model is an enhanced version of the Fast R-CNN model. Due to the computation bottleneck posed by Fast-RCNN that saw the innovation of Region of Pooling. Faster-RCNN introduces the Region of Proposal Network(RPN) and reuses the same CNN results for the same proposal i... | [
"TAGS\n#arxiv-1504.08083 #arxiv-1703.06870 #has_space #region-us \n",
"# Faster R-CNN",
"## Model desription\n\nThis model is an enhanced version of the Fast R-CNN model. Due to the computation bottleneck posed by Fast-RCNN that saw the innovation of Region of Pooling. Faster-RCNN introduces the Region of Propo... |
text2text-generation | 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. -->
# mal-tls-t5-l3
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following resul... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal-tls-t5-l3", "results": []}]} | SharpAI/mal-tls-t5-l3 | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T21:44:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mal-tls-t5-l3
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training... | [
"# mal-tls-t5-l3\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mal-tls-t5-l3\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evalua... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-bert-yoga-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-bert-yoga-finetuned", "results": []}]} | dsantistevan/distilbert-base-uncased-bert-yoga-finetuned | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T21:56:59+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-bert-yoga-finetuned
===========================================
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: 2.2067
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05... |
text2text-generation | 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. -->
# mal-tls-t5-l12
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following resu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal-tls-t5-l12", "results": []}]} | SharpAI/mal-tls-t5-l12 | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-08T22:48:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mal-tls-t5-l12
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trainin... | [
"# mal-tls-t5-l12\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mal-tls-t5-l12\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evalu... |
null | null | Hugging Face's logo
---
tags:
- object-detection
- vision
library_name: mask_rcnn
datasets:
- coco
---
# Mask R-CNN
## Model desription
Mask R-CNN is a model that extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The model locat... | {} | blesot/Mask-RCNN | null | [
"arxiv:1703.06870",
"has_space",
"region:us"
] | null | 2022-08-08T22:54:30+00:00 | [
"1703.06870"
] | [] | TAGS
#arxiv-1703.06870 #has_space #region-us
| Hugging Face's logo
---
tags:
- object-detection
- vision
library_name: mask_rcnn
datasets:
- coco
---
# Mask R-CNN
## Model desription
Mask R-CNN is a model that extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The model locat... | [
"# Mask R-CNN",
"## Model desription\n\nMask R-CNN is a model that extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The model locates pixels of images instead of just bounding boxes as Faster R-CNN was not designed for pixel-t... | [
"TAGS\n#arxiv-1703.06870 #has_space #region-us \n",
"# Mask R-CNN",
"## Model desription\n\nMask R-CNN is a model that extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The model locates pixels of images instead of just bound... |
text2text-generation | transformers |
# BART-large on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@gmail.com (Dương).
### Important note
When finetuning this model on downstream tasks (e.g. text summarization), ensure that your label has the form of `tokenizer.bos_token + target + tokenizer.eos_to... | {"language": "vi"} | VietAI/vi-bartflax-large-news | null | [
"transformers",
"jax",
"tensorboard",
"bart",
"text2text-generation",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-08T23:33:59+00:00 | [] | [
"vi"
] | TAGS
#transformers #jax #tensorboard #bart #text2text-generation #vi #autotrain_compatible #endpoints_compatible #region-us
|
# BART-large on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@URL (Dương).
### Important note
When finetuning this model on downstream tasks (e.g. text summarization), ensure that your label has the form of 'tokenizer.bos_token + target + tokenizer.eos_token' b... | [
"# BART-large on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương).",
"### Important note\nWhen finetuning this model on downstream tasks (e.g. text summarization), ensure that your label has the form of 'tokenizer.bos_token + target + tokenizer... | [
"TAGS\n#transformers #jax #tensorboard #bart #text2text-generation #vi #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART-large on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương).",
"### Important note\nWhen finetuning this ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-2_H-128_A-2-nan-labels-new-longer
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/bert_uncased_L-2_H-128_A-2", "model-index": [{"name": "bert_uncased_L-2_H-128_A-2-nan-labels-new-longer", "results": []}]} | muhtasham/bert-tiny-finetuned-nan-labels-new-longer | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T00:19:50+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-2\_H-128\_A-2-nan-labels-new-longer
====================================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4563
Model description
-----------------
M... | [
"### 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: 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: 1000",
"### Trai... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #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* learn... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-009901
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-009901", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-009901 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T00:41:57+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-009901
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4697
- Bleu: 1.7899
- Gen Len: 46.5638
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-009901\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.4697\n- Bleu: 1.7899\n- Gen Len: 46.5638",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-009901\n\nThis model is a fine-tuned version of google/mt5-small on... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-2_H-128_A-2-finetuned-parsed
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/bert_uncased_L-2_H-128_A-2", "model-index": [{"name": "bert_uncased_L-2_H-128_A-2-finetuned-parsed", "results": []}]} | muhtasham/bert-tiny-finetuned-parsed | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T01:22:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-2\_H-128\_A-2-finetuned-parsed
===============================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2883
Model description
-----------------
More info... | [
"### 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: 200",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-2_H-128_A-2 #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* learn... |
null | FastAI |
# British Columbia Invasive Plants Identifier
## Model Details
An invasive plant classifier trained on BingSearch Images scraped dataset with FastAi.
Model is able to detect 6 species (marked for Provincial Containment) of invasive plants defined by the government of British Columbia.
Hosted in a HuggingFace Space a... | {"library_name": "FastAI", "tags": ["FastAI"]} | et-do/invasive_plant_classifier | null | [
"FastAI",
"region:us"
] | null | 2022-08-09T01:55:02+00:00 | [] | [] | TAGS
#FastAI #region-us
|
# British Columbia Invasive Plants Identifier
## Model Details
An invasive plant classifier trained on BingSearch Images scraped dataset with FastAi.
Model is able to detect 6 species (marked for Provincial Containment) of invasive plants defined by the government of British Columbia.
Hosted in a HuggingFace Space a... | [
"# British Columbia Invasive Plants Identifier",
"## Model Details\n\nAn invasive plant classifier trained on BingSearch Images scraped dataset with FastAi.\nModel is able to detect 6 species (marked for Provincial Containment) of invasive plants defined by the government of British Columbia.\n\nHosted in a Huggi... | [
"TAGS\n#FastAI #region-us \n",
"# British Columbia Invasive Plants Identifier",
"## Model Details\n\nAn invasive plant classifier trained on BingSearch Images scraped dataset with FastAi.\nModel is able to detect 6 species (marked for Provincial Containment) of invasive plants defined by the government of Briti... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-900010
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-900010", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-900010 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T02:02:09+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-900010
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8177
- Bleu: 1.1552
- Gen Len: 98.4712
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-900010\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.8177\n- Bleu: 1.1552\n- Gen Len: 98.4712",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-900010\n\nThis model is a fine-tuned version of google/mt5-small on... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | yokoe/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T03:02:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2109
* Accuracy: 0.9245
* F1: 0.9247
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-1000-samples
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuning-sentiment-model-1000-samples", "results": []}]} | zboxi7/finetuning-sentiment-model-1000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T04:36:18+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-1000-samples
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1604
- Accuracy: 0.74
## Model description
More information needed
## Intended uses & limitations... | [
"# finetuning-sentiment-model-1000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1604\n- Accuracy: 0.74",
"## Model description\n\nMore information needed",
"## Intended u... | [
"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-1000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# relation-detection-prop-16-train-set
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-ca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "relation-detection-prop-16-train-set", "results": []}]} | ultra-coder54732/relation-detection-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T04:47:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# relation-detection-prop-16-train-set
This model is a fine-tuned version of bert-base-cased 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 h... | [
"# relation-detection-prop-16-train-set\n\nThis model is a fine-tuned version of bert-base-cased 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... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# relation-detection-prop-16-train-set\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model descri... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-010099
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-010099 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T04:48:59+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9787
- Bleu: 5.9209
- Gen Len: 50.1856
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# distilled-mt5-small-010099\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.9787\n- Bleu: 5.9209\n- Gen Len: 50.1856",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099\n\nThis model is a fine-tuned version of google/mt5-small on... |
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... | xusysh/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T05:06:03+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. -->
# 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"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {... | haesun/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T05:14:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-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: 0.7681
* Accuracy: 0.9184
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# leabum/distilbert-base-uncased-finetuned-cuad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "leabum/distilbert-base-uncased-finetuned-cuad", "results": []}]} | leabum/distilbert-base-uncased-finetuned-cuad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T05:35:58+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| leabum/distilbert-base-uncased-finetuned-cuad
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5490
* Train End Logits Accuracy: 0.9403
* Train Start Logits Accu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 220, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
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_fr
This model is a fine-tuned version of [zboxi7/finetuning-sentiment-model-3000-samples... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples_fr", "results": []}]} | zboxi7/finetuning-sentiment-model-3000-samples_fr | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T05:50:17+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_fr
This model is a fine-tuned version of zboxi7/finetuning-sentiment-model-3000-samples on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4052
- Accuracy: 0.7033
## Model description
More information needed
## Intended uses & limitat... | [
"# finetuning-sentiment-model-3000-samples_fr\n\nThis model is a fine-tuned version of zboxi7/finetuning-sentiment-model-3000-samples on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.4052\n- Accuracy: 0.7033",
"## Model description\n\nMore information needed",
"## Intend... | [
"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_fr\n\nThis model is a fine-tuned version of zboxi7/finetuning-sentiment-model-3000-samples ... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | gostrive/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T06:01:45+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-hiddentest
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-hiddentest", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": ... | Lvxue/distilled-mt5-small-hiddentest | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T06:39:11+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-hiddentest
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9223
- Bleu: 0.4773
- Gen Len: 51.3902
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# distilled-mt5-small-hiddentest\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.9223\n- Bleu: 0.4773\n- Gen Len: 51.3902",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-hiddentest\n\nThis model is a fine-tuned version of google/mt5-smal... |
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_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "token_fine_tunned_flipkart_2", "results": []}]} | vinayak361/token_fine_tunned_flipkart_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T06:48:03+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\_2
================================
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: 0.3435
* Precision: 0.8797
* Recall: 0.9039
* F1: 0.8916
* Accuracy: 0.9061
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\\_... |
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. -->
# legal-bert-small-uncased-cuad
This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nl... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "legal-bert-small-uncased-cuad", "results": []}]} | alex-apostolo/legal-bert-small-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T07:08:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| legal-bert-small-uncased-cuad
=============================
This model is a fine-tuned version of nlpaueb/legal-bert-small-uncased on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0371
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #license-cc-by-sa-4.0 #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: ... |
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="apurva19/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | apurva19/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-09T07:15:09+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"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-010099-full
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-010099-full", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args":... | Lvxue/distilled-mt5-small-010099-full | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T07:19:17+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-010099-full
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9934
- Bleu: 21.2377
- Gen Len: 44.0745
## Model description
More information needed
## Intended uses & limitations
More informa... | [
"# distilled-mt5-small-010099-full\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.9934\n- Bleu: 21.2377\n- Gen Len: 44.0745",
"## Model description\n\nMore information needed",
"## Intended uses & limitat... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-010099-full\n\nThis model is a fine-tuned version of google/mt5-sma... |
text-generation | transformers |
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました
ファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。
webアプリ<br>
http://www.eswrite.com
The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/) | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "thumbnail": "https://1.bp.blogspot.com/-pOL-P7Mvgkg/YEGQAdidksI/AAAAAAABdc0/SbD0lC_X8iY_t5xLFtQYFC3FHFgziBuzgCNcBGAsYHQ/s932/buranko_businesswoman_sad.png", "widget": [{"text": "\u5fa1\u793e\u3092\u5fd7\u671b\u3057... | huranokuma/es2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T07:20:00+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #ja #japanese #lm #nlp #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました
ファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。
webアプリ<br>
URL
The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd. | [
"# ESを書くAI\nJapanese GPT-2 modelをファインチューニングしました\nファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。\n\nwebアプリ<br>\nURL\n\nThe model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd."
] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #ja #japanese #lm #nlp #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ESを書くAI\nJapanese GPT-2 modelをファインチューニングしました\nファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。\n\nwebアプリ<br>\nURL\n\nThe model wa... |
text2text-generation | transformers |
60c14be097a0f25e5da8f7cca500f6f9
| {"license": "apache-2.0"} | rajkumarrrk/t5-common-gen | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T07:27:27+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
60c14be097a0f25e5da8f7cca500f6f9
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess2
This model is a fine-tuned version of [digitalepidemiologylab/co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess2", "results": []}]} | sumba/covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T07:49:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| covid-twitter-bert-v2-no\_description-stance-loss-hyp-unprocess2
================================================================
This model is a fine-tuned version of digitalepidemiologylab/covid-twitter-bert-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5816
* Accura... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.4275469935864394e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.4275469935864394e-05\n* train... |
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="apurva19/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | apurva19/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-09T07:52:14+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"
] |
null | null |
This is a weights storage for models trained by [ReadingPipeline](https://github.com/ai-forever/ReadingPipeline)
The weights are for ocr and segmentations models trained on [Peter dataset](https://huggingface.co/datasets/sberbank-ai/Peter)
| {"language": ["ru"], "license": "mit", "tags": ["PyTorch", "OCR", "Segmentation", "HTR"], "datasets": ["sberbank-ai/Peter"]} | ai-forever/ReadingPipeline-Peter | null | [
"onnx",
"PyTorch",
"OCR",
"Segmentation",
"HTR",
"ru",
"dataset:sberbank-ai/Peter",
"license:mit",
"region:us"
] | null | 2022-08-09T08:07:45+00:00 | [] | [
"ru"
] | TAGS
#onnx #PyTorch #OCR #Segmentation #HTR #ru #dataset-sberbank-ai/Peter #license-mit #region-us
|
This is a weights storage for models trained by ReadingPipeline
The weights are for ocr and segmentations models trained on Peter dataset
| [] | [
"TAGS\n#onnx #PyTorch #OCR #Segmentation #HTR #ru #dataset-sberbank-ai/Peter #license-mit #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment-10Epochs-3
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unk... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentiment-10Epochs-3", "results": []}]} | sepidmnorozy/sentiment-10Epochs-3 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T08:46:08+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sentiment-10Epochs-3
====================
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: 0.7703
* Accuracy: 0.8568
* F1: 0.8526
* Precision: 0.8787
* Recall: 0.8279
Model description
-----------------
More informatio... | [
"### 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... |
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... | Mahmoud7/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T08:50:56+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. -->
# distilbert-base-uncased-finetuned-ft1500_norm500_aug2-3
This model is a fine-tuned version of [distilbert-base-uncased](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm500_aug2-3", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm500_aug2-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T09:06:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm500\_aug2-3
=========================================================
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: 2.5766
* Mse: 5.1532
* Mae: 1.3526
* R2: -0.0072
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# vit-base-patch16-224-in21k-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type":... | Chandanab/vit-base-patch16-224-in21k-finetuned-eurosat | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T09:15:54+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-patch16-224-in21k-finetuned-eurosat
============================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3648
* Accuracy: 0.9017
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 #vit #image-classification #generated_from_trainer #dataset-image_folder #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: 5e... |
null | null | - tower
| {} | msj/aa | null | [
"region:us"
] | null | 2022-08-09T09:15:59+00:00 | [] | [] | TAGS
#region-us
| - tower
| [] | [
"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
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... | royam0820/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T09:44:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1365
* F1: 0.8649
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers |
This is the OPT model trained in this [video](https://www.youtube.com/watch?v=_GLixmhmdZc)
It has been trained on the complete works of H. P. Lovecraft.
It is both highly overfit and extremely racist.
It can and will use racial slurs given the chance.
To use it, you will have to download it.
Check out my [YouTub... | {"license": "other"} | BearlyWorkingYT/OPT-125M-Lovecraft | null | [
"transformers",
"pytorch",
"tf",
"opt",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T09:52:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #opt #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is the OPT model trained in this video
It has been trained on the complete works of H. P. Lovecraft.
It is both highly overfit and extremely racist.
It can and will use racial slurs given the chance.
To use it, you will have to download it.
Check out my YouTube Channel
Edit: You probably shouldn't use this... | [] | [
"TAGS\n#transformers #pytorch #tf #opt #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# token_fine_tunned_flipkart_2_gl
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "token_fine_tunned_flipkart_2_gl", "results": []}]} | vinayak361/token_fine_tunned_flipkart_2_gl | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T09:53:33+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\_2\_gl
====================================
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: 0.0275
* Precision: 0.9888
* Recall: 0.9900
* F1: 0.9894
* Accuracy: 0.9924
Model descriptio... | [
"### 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\\_... |
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-misogyny-sexism-decay0.05-fr-indomain
This model is a fine-tuned version of [xlm-roberta-base](https://huggingf... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "xlm-roberta-base-misogyny-sexism-decay0.05-fr-indomain", "results": []}]} | annahaz/xlm-roberta-base-misogyny-sexism-decay0.05-fr-indomain | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T10:09:06+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-misogyny-sexism-decay0.05-fr-indomain
======================================================
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: 1.2200
* Accuracy: 0.8708
* F1: 0.0040
* Precision: 0.1
* Recall:... | [
"### 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: 7",
"### Traini... | [
"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* ... |
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"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {... | haesun/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T10:41:01+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #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: 0.0460
* Accuracy: 0.9319
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
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="DennisSoemers/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"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": ... | DennisSoemers/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-09T10:51:16+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"
] |
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="DennisSoemers/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | DennisSoemers/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-09T10:54:01+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"
] |
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. -->
# legal-bert-small-uncased-filtered-filtered-cuad
This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https:/... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["alex-apostolo/filtered-cuad"], "model-index": [{"name": "legal-bert-small-uncased-filtered-filtered-cuad", "results": []}]} | alex-apostolo/legal-bert-small-filtered-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:alex-apostolo/filtered-cuad",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T11:22:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| legal-bert-small-uncased-filtered-filtered-cuad
===============================================
This model is a fine-tuned version of nlpaueb/legal-bert-small-uncased on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0604
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "config": "de... | Bhumika-kumaran/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T11:40:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4789
* Rouge1: 28.2786
* Rouge2: 7.6957
* Rougel: 22.1976
* Rougelsum: 22.2034
* Gen Len: 18.8238
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
reinforcement-learning | stable-baselines3 |
# **MlpPolicy_ppo** Agent playing **LunarLander-v2**
This is a trained model of a **MlpPolicy_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 ...
f... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "MlpPolicy_ppo", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type... | scott-ml/ppo-lunarlander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T11:44:02+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# MlpPolicy_ppo Agent playing LunarLander-v2
This is a trained model of a MlpPolicy_ppo agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# MlpPolicy_ppo Agent playing LunarLander-v2\nThis is a trained model of a MlpPolicy_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",
"# MlpPolicy_ppo Agent playing LunarLander-v2\nThis is a trained model of a MlpPolicy_ppo agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baseli... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-v4-e1-rev
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-v4-e1-rev", "results": []}]} | theojolliffe/bart-paraphrase-v4-e1-rev | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T11:59:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v4-e1-rev
=========================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4221
* Rouge1: 62.2412
* Rouge2: 56.1611
* Rougel: 59.4952
* Rougelsum: 61.581
* Gen Len: 19.6036
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
text2text-generation | 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. -->
# VanessaSchenkel/padrao-mbart-finetuned-opus_books
This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "VanessaSchenkel/padrao-mbart-finetuned-opus_books", "results": []}]} | VanessaSchenkel/padrao-mbart-finetuned-opus_books | null | [
"transformers",
"tf",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T12:02:58+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #mbart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| VanessaSchenkel/padrao-mbart-finetuned-opus\_books
==================================================
This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8117
* Validation Loss... | [
"### 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 #tensorboard #mbart #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate':... |
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": []}]} | Asma-Kehila/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 | 2022-08-09T12:16:00+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: 0.3175
- Accuracy: 0.8733
- F1: 0.8733
## Model description
More information needed
## Intended uses & limitations
More ... | [
"# 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: 0.3175\n- Accuracy: 0.8733\n- F1: 0.8733",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-en-to-ro
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "model-index": [{"name": "bart-base-finetuned-en-to-ro", "results": []}]} | Jinchen/bart-base-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T12:38:00+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #bart #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-base-finetuned-en-to-ro
============================
This model is a fine-tuned version of facebook/bart-base on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9912
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: IPU\n* gradient\\_accumulation\\_steps: 128\n* total\\_train\\_batch\\_size: 512\n* total\\_eval\\_batch\\... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #bart #text2text-generation #generated_from_trainer #dataset-wmt16 #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... |
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. -->
# deit-tiny-patch16-224-finetuned-eurosat
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "deit-tiny-patch16-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "ima... | Chandanab/deit-tiny-patch16-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T12:53:48+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| deit-tiny-patch16-224-finetuned-eurosat
=======================================
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1779
* Accuracy: 0.9192
Model description
-----------------
More i... | [
"### 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 #vit #image-classification #generated_from_trainer #dataset-image_folder #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: 5e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_norm500_aug4
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm500_aug4", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm500_aug4 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T13:01:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm500\_aug4
=======================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5706
* Mse: 3.1412
* Mae: 1.0811
* R2: 0.3860
* Accu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# beit-base-patch16-224-pt22k-finetuned-eurosat
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "beit-base-patch16-224-pt22k-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type"... | Chandanab/beit-base-patch16-224-pt22k-finetuned-eurosat | null | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T13:03:30+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| beit-base-patch16-224-pt22k-finetuned-eurosat
=============================================
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3045
* Accuracy: 0.8586
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 #beit #image-classification #generated_from_trainer #dataset-image_folder #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: 5... |
reinforcement-learning | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'ppo'... | {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"... | workRL/cleanPPOLunar | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-09T13:22:51+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL\n \n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n To learn to code your own PPO agent and train ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_norm500_aug5
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm500_aug5", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm500_aug5 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T13:25:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm500\_aug5
=======================================================
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: 0.8927
* Mse: 2.9755
* Mae: 1.0176
* R2: 0.4184
* Accu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
# 🦾 xlm-roberta-large-squad2-ctkfacts
## 🧰 Usage
### 🤗 Using Huggingface `transformers`
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts")
tokenizer = AutoTokenizer.from_pretra... | {"license": "cc-by-sa-4.0", "tags": ["natural-language-inference"], "datasets": ["ctkfacts", "squad2"], "languages": ["cs"]} | ctu-aic/xlm-roberta-large-squad2-ctkfacts | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"natural-language-inference",
"dataset:ctkfacts",
"dataset:squad2",
"arxiv:2201.11115",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T13:40:12+00:00 | [
"2201.11115"
] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #natural-language-inference #dataset-ctkfacts #dataset-squad2 #arxiv-2201.11115 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-large-squad2-ctkfacts
## Usage
### Using Huggingface 'transformers'
### Using UKPLab 'sentence_transformers' 'CrossEncoder'
The model was trained using the 'CrossEncoder' API and we recommend it for its usage.
## Contributing
Pull requests are welcome. For major changes, please open an issue f... | [
"# xlm-roberta-large-squad2-ctkfacts",
"## Usage",
"### Using Huggingface 'transformers'",
"### Using UKPLab 'sentence_transformers' 'CrossEncoder'\nThe model was trained using the 'CrossEncoder' API and we recommend it for its usage.",
"## Contributing\nPull requests are welcome. For major changes, pl... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #natural-language-inference #dataset-ctkfacts #dataset-squad2 #arxiv-2201.11115 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-large-squad2-ctkfacts",
"## Usage",
"### Using Huggingface 'tr... |
text-generation | transformers |
# OPT-1.3B fine-tuned on officially translated light novels.
Fine-tuned [OPT-1.3b](https://huggingface.co/facebook/opt-1.3b) on 2,500 light novel volumes (~825MB of text).
| Epoch | Test Loss | Train Loss |
|--------------------|-----------|------------|
| 0 (Not fine-tuned) | 4.9080 | N/A |
|... | {"language": "en", "license": "other", "tags": ["text-generation", "opt"], "inference": false, "commercial": false} | thefrigidliquidation/opt-1.3b-lightnovels | null | [
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T14:06:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #opt #text-generation #en #license-other #autotrain_compatible #text-generation-inference #region-us
| OPT-1.3B fine-tuned on officially translated light novels.
==========================================================
Fine-tuned OPT-1.3b on 2,500 light novel volumes (~825MB of text).
Epoch: 0 (Not fine-tuned), Test Loss: 4.9080, Train Loss: N/A
Epoch: 1, Test Loss: 2.1659, Train Loss: 2.0625
Epoch: 2, Test Loss: ... | [] | [
"TAGS\n#transformers #pytorch #opt #text-generation #en #license-other #autotrain_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | ('---\ndatasets:\n- ctu-aic/csfever\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# 🦾 xlm-roberta-large-xnli-csfever
Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/csfever'] datasets.
## 🧰 Usage
### 👾 Using UKPLab `sentence... | {"datasets": "ctu-aic/csfever"} | ctu-aic/xlm-roberta-large-xnli-csfever | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"dataset:ctu-aic/csfever",
"arxiv:2201.11115",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T14:11:24+00:00 | [
"2201.11115"
] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #dataset-ctu-aic/csfever #arxiv-2201.11115 #autotrain_compatible #endpoints_compatible #region-us
| ('---\ndatasets:\n- ctu-aic/csfever\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# xlm-roberta-large-xnli-csfever
Transformer model for Natural Language Inference in ['cs'] languages finetuned on ['ctu-aic/csfever'] datasets.
## Usage
### Using UKPLab 'sentence_transform... | [
"# xlm-roberta-large-xnli-csfever\nTransformer model for Natural Language Inference in ['cs'] languages finetuned on ['ctu-aic/csfever'] datasets.",
"## Usage",
"### Using UKPLab 'sentence_transformers' 'CrossEncoder'\nThe model was trained using the 'CrossEncoder' API and we recommend it for its usage.",
... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #dataset-ctu-aic/csfever #arxiv-2201.11115 #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-large-xnli-csfever\nTransformer model for Natural Language Inference in ['cs'] languages finetuned on ['ctu-aic/csfever'] datas... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-v8-e1-rev
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-v8-e1-rev", "results": []}]} | theojolliffe/bart-paraphrase-v8-e1-rev | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T14:12:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v8-e1-rev
=========================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2811
* Rouge1: 59.7973
* Rouge2: 54.3079
* Rougel: 56.5768
* Rougelsum: 59.4379
* Gen Len: 19.8108
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-korean-demo-no-LM
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-korean-demo-no-LM", "results": []}]} | NX2411/wav2vec2-large-xlsr-korean-demo-no-LM | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T14:22:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-korean-demo-no-LM
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3215
* Wer: 0.2209
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0003\n* train\\_batch\\_size: 4... |
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="butchland/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"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": ... | butchland/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-09T14:35:38+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"
] |
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="butchland/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": "7.44 +/... | butchland/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-09T14:38:10+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# student_marian_en_ro_6_1
This model is a fine-tuned version of [sshleifer/student_marian_en_ro_6_1](https://huggingface.co/sshleifer/s... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "student_marian_en_ro_6_1", "results": []}]} | Rocketknight1/student_marian_en_ro_6_1 | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T14:42:56+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# student_marian_en_ro_6_1
This model is a fine-tuned version of sshleifer/student_marian_en_ro_6_1 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation dat... | [
"# student_marian_en_ro_6_1\n\nThis model is a fine-tuned version of sshleifer/student_marian_en_ro_6_1 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trainin... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# student_marian_en_ro_6_1\n\nThis model is a fine-tuned version of sshleifer/student_marian_en_ro_6_1 on an unknown dataset.\nIt achieves the following results on the... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481
## Validation Metrics
- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision:... | {"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["jfan-98/autotrain-data-LegalLong_on_contracts"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 47.64808387548789}} | jfan-98/autotrain-LegalLong_on_contracts-1230246837 | null | [
"transformers",
"pytorch",
"longformer",
"text-classification",
"autotrain",
"unk",
"dataset:jfan-98/autotrain-data-LegalLong_on_contracts",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T14:57:52+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #longformer #text-classification #autotrain #unk #dataset-jfan-98/autotrain-data-LegalLong_on_contracts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1230246837
- CO2 Emissions (in grams): 47.6481
## Validation Metrics
- Loss: 0.265
- Accuracy: 0.943
- Macro F1: 0.944
- Micro F1: 0.943
- Weighted F1: 0.943
- Macro Precision: 0.947
- Micro Precision: 0.943
- Weighted Precision:... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1230246837\n- CO2 Emissions (in grams): 47.6481",
"## Validation Metrics\n\n- Loss: 0.265\n- Accuracy: 0.943\n- Macro F1: 0.944\n- Micro F1: 0.943\n- Weighted F1: 0.943\n- Macro Precision: 0.947\n- Micro Precision: 0.943\n... | [
"TAGS\n#transformers #pytorch #longformer #text-classification #autotrain #unk #dataset-jfan-98/autotrain-data-LegalLong_on_contracts #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1230246837... |
text2text-generation | transformers | # MarianNMT-Tatoeba-lb-en | {"language": ["lb", "en"], "license": "cc-by-nc-sa-4.0", "tags": ["Translation", "MarianNMT"], "datasets": ["mbarnig/Tatoeba-en-lb"], "language_details": "ltz_Latn, eng_Latn"} | mbarnig/marianNMT-tatoeba-lb-en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"Translation",
"MarianNMT",
"lb",
"en",
"dataset:mbarnig/Tatoeba-en-lb",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-09T15:06:51+00:00 | [] | [
"lb",
"en"
] | TAGS
#transformers #pytorch #marian #text2text-generation #Translation #MarianNMT #lb #en #dataset-mbarnig/Tatoeba-en-lb #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # MarianNMT-Tatoeba-lb-en | [
"# MarianNMT-Tatoeba-lb-en"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #Translation #MarianNMT #lb #en #dataset-mbarnig/Tatoeba-en-lb #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MarianNMT-Tatoeba-lb-en"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | spacemanidol/esci-mpnet-crossencoder | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:20:29+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | spacemanidol/esci-jp-mpnet-crossencoder | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:21:34+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | spacemanidol/esci-es-mpnet-crossencoder | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:21:43+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or 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. -->
# bert-base-qa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-qa", "results": []}]} | srcocotero/bert-base-qa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:25:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-qa
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
"# bert-base-qa\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-qa\n\nThis model is a fine-tuned version of bert-base-uncased on the squad dataset.",
"## Model description\n\nMore information neede... |
text-classification | null |
# EMO demo 00
## TODO
### incorporate with EMO_AI
### put pretrained weight here | {"license": "apache-2.0", "tags": ["text-classification"], "widget": [{"text": "This love has taken its toll on me", "example_title": "sadness"}]} | KLeedrug/EMO_demo_00 | null | [
"text-classification",
"license:apache-2.0",
"region:us"
] | null | 2022-08-09T15:26:58+00:00 | [] | [] | TAGS
#text-classification #license-apache-2.0 #region-us
|
# EMO demo 00
## TODO
### incorporate with EMO_AI
### put pretrained weight here | [
"# EMO demo 00",
"## TODO",
"### incorporate with EMO_AI",
"### put pretrained weight here"
] | [
"TAGS\n#text-classification #license-apache-2.0 #region-us \n",
"# EMO demo 00",
"## TODO",
"### incorporate with EMO_AI",
"### put pretrained weight here"
] |
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... | aya-se/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:48:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2064
* Accuracy: 0.922
* F1: 0.9226
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relb... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"... | research-backup/roberta-large-conceptnet-average-no-mask-prompt-c-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:52:12+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset... | [
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Questi... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning... |
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... | LilOpa/LunarLanderPPO_new | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T15:55:22+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"
] | [
"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 the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-my_dear_watson
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-ro... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-my_dear_watson", "results": []}]} | SmartPy/xlm-roberta-base-finetuned-my_dear_watson | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T15:57:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-my\_dear\_watson
===========================================
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: 1.8264
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #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\... |
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... | AG16/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T16:13:23+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... |
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... | LilOpa/LunarLanderPPO_new-2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T16:26:18+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 |
# Schiappa-Minelli GPT-2
Pourquoi pas ?
## Dataset
- Marianne est déchainée, de Marlène Schiappa
- Osez les sexfriends, Marie Minelli
- Osez réussir votre divorce, Marie Minelli
- Sexe, mensonge et banlieues chaudes, Marie Minelli
## Versions
V1:
- Fine-tunée avec [Max Woolf's "aitextgen — Train a GPT-2 (or GPT N... | {"license": "unknown"} | href/gpt2-schiappa | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T16:30:28+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-unknown #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Schiappa-Minelli GPT-2
Pourquoi pas ?
## Dataset
- Marianne est déchainée, de Marlène Schiappa
- Osez les sexfriends, Marie Minelli
- Osez réussir votre divorce, Marie Minelli
- Sexe, mensonge et banlieues chaudes, Marie Minelli
## Versions
V1:
- Fine-tunée avec Max Woolf's "aitextgen — Train a GPT-2 (or GPT Ne... | [
"# Schiappa-Minelli GPT-2\n\nPourquoi pas ?",
"## Dataset\n\n- Marianne est déchainée, de Marlène Schiappa\n- Osez les sexfriends, Marie Minelli\n- Osez réussir votre divorce, Marie Minelli\n- Sexe, mensonge et banlieues chaudes, Marie Minelli",
"## Versions\n\nV1:\n- Fine-tunée avec Max Woolf's \"aitextgen — T... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-unknown #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Schiappa-Minelli GPT-2\n\nPourquoi pas ?",
"## Dataset\n\n- Marianne est déchainée, de Marlène Schiappa\n- Osez les sexfriends, Marie Minelli... |
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. -->
# qs-classifier-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "qs-classifier-bert", "results": []}]} | asvs/qs-classifier-bert | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T16:33:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| qs-classifier-bert
==================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# qs-classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "qs-classifier", "results": []}]} | asvs/qs-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T17:00:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| qs-classifier
=============
This model is a fine-tuned version of bert-base-uncased on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | aks234/pegasus-samsum | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T17:03:41+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum 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 #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore informat... |
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... | LilOpa/LunarLanderPPO_new-3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T17:32:54+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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "config": "de... | aks234/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-09T17:45:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4785
* Rouge1: 28.3127
* Rouge2: 7.7376
* Rougel: 22.2445
* Rougelsum: 22.2505
* Gen Len: 18.8304
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dna_bert_3_2-finetuned
This model is a fine-tuned version of [armheb/DNA_bert_3](https://huggingface.co/armheb/DNA_bert_3) on an... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dna_bert_3_2-finetuned", "results": []}]} | Mozart-coder/dna_bert_3_2-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T17:50:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| dna\_bert\_3\_2-finetuned
=========================
This model is a fine-tuned version of armheb/DNA\_bert\_3 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4668
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_si... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNADebertaSentencepiece30k_continuation_continuation_continuation
This model is a fine-tuned version of [Vlasta/DNADebertaSenten... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaSentencepiece30k_continuation_continuation_continuation", "results": []}]} | Vlasta/DNADebertaSentencepiece30k_continuation_continuation_continuation | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T17:52:18+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNADebertaSentencepiece30k\_continuation\_continuation\_continuation
====================================================================
This model is a fine-tuned version of Vlasta/DNADebertaSentencepiece30k\_continuation\_continuation on the None dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNADebertaSentencepiece10k_continuation_continuation_continuation
This model is a fine-tuned version of [Vlasta/DNADebertaSenten... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaSentencepiece10k_continuation_continuation_continuation", "results": []}]} | Vlasta/DNADebertaSentencepiece10k_continuation_continuation_continuation | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T17:52:25+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNADebertaSentencepiece10k\_continuation\_continuation\_continuation
====================================================================
This model is a fine-tuned version of Vlasta/DNADebertaSentencepiece10k\_continuation\_continuation on the None dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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-large-finetuned-syndag-multiclass-not-bloom
This model is a fine-tuned version of [microsoft/deberta-v3-large](https:... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-syndag-multiclass-not-bloom", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-syndag-multiclass-not-bloom | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T17:52:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-syndag-multiclass-not-bloom
======================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0197
* F1: 0.9956
* Precision: 0.9956
* Recall: 0.9... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-financial-phrasebank-allagree2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["financial_phrasebank"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-large-uncased-financial-phrasebank-allagree2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "finan... | Farshid/bert-large-uncased-financial-phrasebank-allagree2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:financial_phrasebank",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T18:00:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-financial-phrasebank-allagree2
=================================================
This model is a fine-tuned version of bert-large-uncased on the financial\_phrasebank dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0734
* Accuracy: 0.9912
* F1: 0.9911
Model descripti... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-financial_phrasebank #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... |
text-classification | transformers | ## Arabic-MARBERT-dialect-Identification-City Model
#### Model description
**arabic-MARBERT-dialect-identification-city Model** is a dialect identification model that was built by fine-tuning the [MARBERT](https://huggingface.co/UBC-NLP/MARBERT) model. For the fine-tuning, I used [MADAR Corpus 26 dataset](https://camel... | {"language": ["ar"], "tags": ["text classification"], "widget": [{"text": "\u0645\u0627 \u0634\u0641\u062a \u0647\u062f\u0627 \u0627\u0644\u0639\u0646\u0648\u0627\u0646 \u0647\u0648\u0646"}, {"text": "\u0645\u0628\u0642\u062f\u0631\u0634 \u0627\u062d\u0627\u0641\u0638 \u0639\u0644\u064a \u0627\u0644\u0645\u0633\u062a\u... | Ammar-alhaj-ali/arabic-MARBERT-dialect-identification-city | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text classification",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T18:10:53+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #text classification #ar #autotrain_compatible #endpoints_compatible #region-us
| ## Arabic-MARBERT-dialect-Identification-City Model
#### Model description
arabic-MARBERT-dialect-identification-city Model is a dialect identification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used MADAR Corpus 26 dataset, which includes 26 labels(cities).
#### How to use
To use th... | [
"## Arabic-MARBERT-dialect-Identification-City Model",
"#### Model description\narabic-MARBERT-dialect-identification-city Model is a dialect identification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used MADAR Corpus 26 dataset, which includes 26 labels(cities).",
"#### How t... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text classification #ar #autotrain_compatible #endpoints_compatible #region-us \n",
"## Arabic-MARBERT-dialect-Identification-City Model",
"#### Model description\narabic-MARBERT-dialect-identification-city Model is a dialect identification model that wa... |
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. -->
# legal-roberta-base-cuad
This model is a fine-tuned version of [saibo/legal-roberta-base](https://huggingface.co/saibo/legal-robe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "legal-roberta-base-cuad", "results": []}]} | alex-apostolo/legal-roberta-base-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T18:13:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-cuad #license-apache-2.0 #endpoints_compatible #region-us
| legal-roberta-base-cuad
=======================
This model is a fine-tuned version of saibo/legal-roberta-base on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0260
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #roberta #question-answering #generated_from_trainer #dataset-cuad #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:... |
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. -->
# legal-roberta-base-filtered-cuad
This model is a fine-tuned version of [saibo/legal-roberta-base](https://huggingface.co/saibo/l... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["alex-apostolo/filtered-cuad"], "model-index": [{"name": "legal-roberta-base-filtered-cuad", "results": []}]} | alex-apostolo/legal-roberta-base-filtered-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:alex-apostolo/filtered-cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-09T18:16:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #license-apache-2.0 #endpoints_compatible #region-us
| legal-roberta-base-filtered-cuad
================================
This model is a fine-tuned version of saibo/legal-roberta-base on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0428
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 22\n* eval\\_batch\\_size: 22\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 #roberta #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #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... |
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... | LilOpa/LunarLanderPPO_new-4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-09T18:30:25+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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.