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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | Keneston/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T18:52:55+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1608
* F1: 0.8593
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text2text-generation | transformers | # Introduction
The automatic paraphrasing model described and used in the paper
"[AutoQA: From Databases to QA Semantic Parsers with Only Synthetic Training Data](https://arxiv.org/abs/2010.04806)" (EMNLP 2020).
# Training data
A cleaned version of the ParaBank 2 dataset introduced in "[Large-Scale, Diverse, Paraphra... | {"license": "apache-2.0"} | stanford-oval/paraphraser-bart-large | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2010.04806",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T18:54:52+00:00 | [
"2010.04806"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2010.04806 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Introduction
The automatic paraphrasing model described and used in the paper
"AutoQA: From Databases to QA Semantic Parsers with Only Synthetic Training Data" (EMNLP 2020).
# Training data
A cleaned version of the ParaBank 2 dataset introduced in "Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Cluster... | [
"# Introduction\nThe automatic paraphrasing model described and used in the paper\n\"AutoQA: From Databases to QA Semantic Parsers with Only Synthetic Training Data\" (EMNLP 2020).",
"# Training data \nA cleaned version of the ParaBank 2 dataset introduced in \"Large-Scale, Diverse, Paraphrastic Bitexts via Sampl... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-2010.04806 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Introduction\nThe automatic paraphrasing model described and used in the paper\n\"AutoQA: From Databases to QA Semantic Parsers with Only Synthetic Trainin... |
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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli", "results": []}]} | omriuz/distilbert-base-uncased-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T18:57:16+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-mnli
======================================
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: 0.8311
* Accuracy: 0.6574
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | skr1125/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T19:06:07+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1654
* F1: 0.8590
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | skr1125/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T19:30:21+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2851
* F1: 0.8331
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | skr1125/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T19:47:33+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2401
* F1: 0.8246
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Hazam/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T20:02:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | skr1125/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T20:03:08+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3932
* F1: 0.7032
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | skr1125/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T20:20:17+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1781
* F1: 0.8538
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
null | nemo | ## Model Overview
This model is a Morse Code recognition model. It was trained with the package at https://github.com/1-800-BAD-CODE/MorseCodeToolkit.
This model accepts as input audio signals sampled at 8khz containing Morse code. The model produces the English transcription of the Morse code signal.
For inference,... | {} | 1-800-BAD-CODE/morsecode_en_quartznet_10x5 | null | [
"nemo",
"region:us"
] | null | 2022-08-05T20:31:47+00:00 | [] | [] | TAGS
#nemo #region-us
| ## Model Overview
This model is a Morse Code recognition model. It was trained with the package at URL
This model accepts as input audio signals sampled at 8khz containing Morse code. The model produces the English transcription of the Morse code signal.
For inference, only the base NeMo package needs to be installe... | [
"## Model Overview\n\nThis model is a Morse Code recognition model. It was trained with the package at URL\n\nThis model accepts as input audio signals sampled at 8khz containing Morse code. The model produces the English transcription of the Morse code signal.\n\nFor inference, only the base NeMo package needs to ... | [
"TAGS\n#nemo #region-us \n",
"## Model Overview\n\nThis model is a Morse Code recognition model. It was trained with the package at URL\n\nThis model accepts as input audio signals sampled at 8khz containing Morse code. The model produces the English transcription of the Morse code signal.\n\nFor inference, only ... |
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... | damilare-akin/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-05T20:38:58+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 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln61Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln61Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInform... | {} | BigSalmon/InformalToFormalLincoln61Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T21:11:12+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different down... | {"license": "apache-2.0"} | Habana/vit | null | [
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-08-05T21:23:55+00:00 | [] | [] | TAGS
#optimum_habana #license-apache-2.0 #region-us
|
Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advant... | [
"## ViT model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the ViT model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': whether to use Habana's custom AdamW implementation\n- 'use_f... | [
"TAGS\n#optimum_habana #license-apache-2.0 #region-us \n",
"## ViT model HPU configuration\n\nThis model only contains the 'GaudiConfig' file for running the ViT model on Habana's Gaudi processors (HPU).\n\nThis model contains no model weights, only a GaudiConfig.\n\nThis enables to specify:\n- 'use_fused_adam': ... |
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. -->
# roberta-base-EnglishLawAI_roberta_base_version4
This model is a fine-tuned version of [roberta-base](https://huggingface.co/robe... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-EnglishLawAI_roberta_base_version4", "results": []}]} | Makabaka/roberta-base-EnglishLawAI_roberta_base_version4 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-05T21:26:38+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-EnglishLawAI\_roberta\_base\_version4
==================================================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8089
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 12",
"### Train... | [
"TAGS\n#transformers #pytorch #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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_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/1338527116644806663/Xkhj... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chipflake/1659739094566/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chipflake | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T21:37:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
the one, singular chip
@chipflake
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | 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"... | trtd56/ppo-LunarLander | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-05T22:11:45+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL\n \n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n To learn to code your own PPO agent and train ... |
reinforcement-learning | null |
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
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'
'seed... | {"tags": ["CartPole-v1", "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": "CartPole-v1", "type": "CartPole-v1"}, "metri... | trtd56/ppo-CartPole | null | [
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-05T22:42:30+00:00 | [] | [] | TAGS
#tensorboard #CartPole-v1 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: URL
# Hyperparameters
| [
"# PPO Agent Playing CartPole-v1\n\n This is a trained model of a PPO agent playing CartPole-v1.\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 #CartPole-v1 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# PPO Agent Playing CartPole-v1\n\n This is a trained model of a PPO agent playing CartPole-v1.\n To learn to code your own PPO agent and train it Unit 8... |
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/804990434455887872/BG0Xh... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/sama | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T23:07:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Sam Altman
@sama
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1484514513651220489/svAJ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/shyamalanadkat/1659744994175/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/shyamalanadkat | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-05T23:16:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Shyamal Hitesh Anadkat
@shyamalanadkat
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# MonoGPTari-1.3b
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an english monogatari text dataset.
This was primarily used as a PoC, use the 6.7b for optimal spiciness.
It achieves the following results on the evaluation set:
- Loss: 1.1909
- Accuracy: 0.7... | {"license": "other", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "output", "results": []}]} | inarikami/monogptari-1.3b | null | [
"transformers",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T00:00:36+00:00 | [] | [] | TAGS
#transformers #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MonoGPTari-1.3b
This model is a fine-tuned version of facebook/opt-1.3b on an english monogatari text dataset.
This was primarily used as a PoC, use the 6.7b for optimal spiciness.
It achieves the following results on the evaluation set:
- Loss: 1.1909
- Accuracy: 0.7299
## Quick start
## Model description
... | [
"# MonoGPTari-1.3b\n\nThis model is a fine-tuned version of facebook/opt-1.3b on an english monogatari text dataset.\n\nThis was primarily used as a PoC, use the 6.7b for optimal spiciness.\n\nIt achieves the following results on the evaluation set:\n- Loss: 1.1909\n- Accuracy: 0.7299",
"## Quick start",
"## Mo... | [
"TAGS\n#transformers #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MonoGPTari-1.3b\n\nThis model is a fine-tuned version of facebook/opt-1.3b on an english monogatari text dataset.\n\nThis was primarily used a... |
text-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. -->
# monogptari-6.7b
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on an englis... | {"license": "other", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "output_2", "results": []}]} | inarikami/monogptari-6.7b | null | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T01:05:32+00:00 | [] | [] | TAGS
#transformers #pytorch #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# monogptari-6.7b
This model is a fine-tuned version of facebook/opt-6.7b on an english monogatari (物語) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7030
- Accuracy: 0.8436
## Quick start
## Model description
More information needed
## Intended uses & limitations
More informat... | [
"# monogptari-6.7b\n\nThis model is a fine-tuned version of facebook/opt-6.7b on an english monogatari (物語) dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7030\n- Accuracy: 0.8436",
"## Quick start",
"## Model description\n\nMore information needed",
"## Intended uses & limitati... | [
"TAGS\n#transformers #pytorch #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# monogptari-6.7b\n\nThis model is a fine-tuned version of facebook/opt-6.7b on an english monogatari (物語) dataset.\nIt achieves the fo... |
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... | parnyanp/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 | 2022-08-06T05:44:22+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: 0.2182
* Accuracy: 0.9275
* F1: 0.9275
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... |
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. -->
# final_headline_generator_depreciated
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/go... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "final_headline_generator_depreciated", "results": []}]} | valurank/final_headline_generator | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-06T05:46:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
| final\_headline\_generator\_depreciated
=======================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1729
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #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... |
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. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | sutd-ai/albert-base-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T07:12:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| albert-base-v2-finetuned-squad
==============================
This model is a fine-tuned version of albert-base-v2 on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9650
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_v2 #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\\_si... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-mask-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) librar... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-mask-prompt-d-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics... | research-backup/roberta-large-conceptnet-mask-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T07:31:35+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-mask-prompt-d-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full resu... | [
"# relbert/roberta-large-conceptnet-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-mask-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done vi... |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1224546522
- CO2 Emissions (in grams): 35.7815
## Validation Metrics
- Loss: 0.638
- Rouge1: 44.532
- Rouge2: 33.731
- RougeL: 40.372
- RougeLsum: 40.653
- Gen Len: 57.730
## Usage
You can use cURL to access this model:
```
$ curl -X POST ... | {"language": ["en"], "tags": ["autotrain", "summarization"], "datasets": ["Akbar-Ali/autotrain-data-News_Summariser_Eng"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 35.7814981860994}} | Akbar-Ali/autotrain-News_Summariser_Eng-1224546522 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain",
"summarization",
"en",
"dataset:Akbar-Ali/autotrain-data-News_Summariser_Eng",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T08:16:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain #summarization #en #dataset-Akbar-Ali/autotrain-data-News_Summariser_Eng #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1224546522
- CO2 Emissions (in grams): 35.7815
## Validation Metrics
- Loss: 0.638
- Rouge1: 44.532
- Rouge2: 33.731
- RougeL: 40.372
- RougeLsum: 40.653
- Gen Len: 57.730
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1224546522\n- CO2 Emissions (in grams): 35.7815",
"## Validation Metrics\n\n- Loss: 0.638\n- Rouge1: 44.532\n- Rouge2: 33.731\n- RougeL: 40.372\n- RougeLsum: 40.653\n- Gen Len: 57.730",
"## Usage\n\nYou can use cURL to access this mo... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain #summarization #en #dataset-Akbar-Ali/autotrain-data-News_Summariser_Eng #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1224546522\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-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]} | yewwdunsay/t5-end2end-questions-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T08:54:00+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-end2end-questions-generation
This model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# t5-end2end-questions-generation\n\nThis model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-end2end-questions-generation\n\nThis model is a fine-tuned version of t5-base on the sq... |
null | null |
# ACGTTS 模型库
### old支持的语音
```
0 - 绫地宁宁
1 - 因幡巡
2 - 户隐憧子
```
### new支持的语音
```
0 - 绫地宁宁
1 - 户隐憧子
2 - 因幡巡
3 - 明月栞那
4 - 四季夏目
5 - 墨染希
6 - 火打谷爱衣
7 - 汐山凉音
8 - 中文注入声线
9 - 二条院羽月
10 - 在原七海
11 - 式部茉优
12 - 三司绫濑
13 - 壬生千咲
14 - 朝武芳乃
15 - 常陆茉子
16 - 丛雨
17 - 蕾娜·列支敦瑙尔
18 - 鞍马小春
```
目前模型支持的语言有中文(方言味浓重)和日语
# 代码地址
[ACGTTS](https://githu... | {"license": "cc-by-nc-sa-4.0"} | chinoll/ACGTTS | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-08-06T09:02:11+00:00 | [] | [] | TAGS
#license-cc-by-nc-sa-4.0 #region-us
|
# ACGTTS 模型库
### old支持的语音
### new支持的语音
目前模型支持的语言有中文(方言味浓重)和日语
# 代码地址
ACGTTS | [
"# ACGTTS 模型库",
"### old支持的语音",
"### new支持的语音\n\n\n目前模型支持的语言有中文(方言味浓重)和日语",
"# 代码地址\nACGTTS"
] | [
"TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n",
"# ACGTTS 模型库",
"### old支持的语音",
"### new支持的语音\n\n\n目前模型支持的语言有中文(方言味浓重)和日语",
"# 代码地址\nACGTTS"
] |
text-classification | transformers |
BEEP! 데이터셋으로 Epoch 10으로 파인튜닝하여 결과를 살펴보겠습니다.
| | Loss | Acc | Prec | Rec | F1 |
|-----|------|-------|------|-------|-------|
|TRAIN| 0.11 | 0.965 | 0.966| 0.972 | 0.969 |
| VAL | 0.73 | 0.807 | 0.947| 0.749 | 0.837 |
threshold 0.5 기준으로 구분하였을 때, dev 데이터셋에 대한 정확도는 0.85 입니다.
그리고 임베딩 결과물을 t-SNE로 시각화하여보았습니다.
... | {"license": "mit"} | koorukuroo/KcELECTRA_base_beep | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T09:20:25+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BEEP! 데이터셋으로 Epoch 10으로 파인튜닝하여 결과를 살펴보겠습니다.
threshold 0.5 기준으로 구분하였을 때, dev 데이터셋에 대한 정확도는 0.85 입니다.
그리고 임베딩 결과물을 t-SNE로 시각화하여보았습니다.
URL
| [] | [
"TAGS\n#transformers #pytorch #electra #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | lurker18/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 | 2022-08-06T09:20:34+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: 0.2220
* Accuracy: 0.9215
* F1: 0.9216
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... |
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_large_CNN_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/BART_large_CNN_GNAD](https://huggingface.co/Einmalumd... | {"language": ["de"], "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_large_CNN_GNAD", "results": []}]} | Einmalumdiewelt/BART_large_CNN_GNAD | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-06T09:52:58+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BART_large_CNN_GNAD
This model is a fine-tuned version of Einmalumdiewelt/BART_large_CNN_GNAD on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9761
- Rouge1: 27.0918
- Rouge2: 7.9818
- Rougel: 17.7781
- Rougelsum: 22.6727
- Gen Len: 96.0567
## Model description
More info... | [
"# BART_large_CNN_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/BART_large_CNN_GNAD on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.9761\n- Rouge1: 27.0918\n- Rouge2: 7.9818\n- Rougel: 17.7781\n- Rougelsum: 22.6727\n- Gen Len: 96.0567",
"## Model descrip... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BART_large_CNN_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/BART_large_CNN_GNAD on an unknown dataset.\nIt achieves the following results... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | AdamAbate1/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T10:29:56+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0993
* Accuracy: 0.9812
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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 #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #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: 3e-05\n* train\\_batch\\_size: 32\n* e... |
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-v1-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-v1-e1", "results": []}]} | theojolliffe/bart-paraphrase-v1-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T12:13:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v1-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: 0.1525
* Rouge1: 71.1522
* Rouge2: 65.1426
* Rougel: 68.9323
* Rougelsum: 69.2231
* Gen Len: 19.4302
Model descriptio... | [
"### 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 |
# distilgpt2-emailgen
Why write the rest of your email when you can generate it?
```python
from transformers import pipeline
model_tag = "postbot/distilgpt2-emailgen"
generator = pipeline(
'text-generation',
model=model_tag,
)
prompt = """
Hello,
Following u... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "distilgpt2", "email generation", "email"], "datasets": ["aeslc", "postbot/multi_emails"], "widget": [{"text": "Good Morning Professor Beans,\nHope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam", "example_... | postbot/distilgpt2-emailgen | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"distilgpt2",
"email generation",
"email",
"dataset:aeslc",
"dataset:postbot/multi_emails",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has... | null | 2022-08-06T12:40:55+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #distilgpt2 #email generation #email #dataset-aeslc #dataset-postbot/multi_emails #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| distilgpt2-emailgen
===================
Why write the rest of your email when you can generate it?
* try it in a Google Colab notebook
* Use it in bash/cmd with this gist :)
>
> For this model, formatting matters. The results may be (significantly) different between the structure outlined above and 'prompt = "H... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam w... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #distilgpt2 #email generation #email #dataset-aeslc #dataset-postbot/multi_emails #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"... |
translation | transformers |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
## PreTraining
The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
Thereby, the following datasets were being used for (1.) and (2.):
1. **Datasets used for Unsupervised ... | {"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | qiaoyi/Comment_Summarization4DesignTutor | null | [
"transformers",
"pytorch",
"jax",
"rust",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.1004... | null | 2022-08-06T13:06:42+00:00 | [
"1805.12471",
"1708.00055",
"1704.05426",
"1606.05250",
"1808.09121",
"1810.12885",
"1905.10044",
"1910.10683"
] | [
"en",
"fr",
"ro",
"de"
] | TAGS
#transformers #pytorch #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1805.12471 #arxiv-1708.00055 #arxiv-1704.05426 #arxiv-1606.05250 #arxiv-1808.09121 #arxiv-1810.12885 #arxiv-1905.10044 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_co... |
Google's T5
## PreTraining
The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).
Thereby, the following datasets were being used for (1.) and (2.):
1. Datasets used for Unsupervised denoising objective:
- C4
- Wiki-DPR
2. Datasets used for Supervised text-to-text... | [
"## PreTraining\n\nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\nThereby, the following datasets were being used for (1.) and (2.):\n\n1. Datasets used for Unsupervised denoising objective:\n\n- C4\n- Wiki-DPR\n\n\n2. Datasets used for Supervised text-to-tex... | [
"TAGS\n#transformers #pytorch #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1805.12471 #arxiv-1708.00055 #arxiv-1704.05426 #arxiv-1606.05250 #arxiv-1808.09121 #arxiv-1810.12885 #arxiv-1905.10044 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoi... |
text2text-generation | transformers | # Kogi Python-Code Generation Model | {"language": "ja", "license": "cc-by-sa-4.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["mc4"]} | kkuramitsu/mt5-kogi-regio | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"t5",
"seq2seq",
"ja",
"dataset:mc4",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T13:46:28+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #t5 #seq2seq #ja #dataset-mc4 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Kogi Python-Code Generation Model | [
"# Kogi Python-Code Generation Model"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #t5 #seq2seq #ja #dataset-mc4 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kogi Python-Code Generation Model"
] |
fill-mask | transformers | Requirement Engineering Domains
Trained for MLM using Pytorch
| {"license": "afl-3.0"} | yohannesSM/re-bert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T13:58:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Requirement Engineering Domains
Trained for MLM using Pytorch
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #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-ema-pets-64
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggin... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "pcuenq/oxford-pets", "metrics": []} | pcuenq/ddpm-ema-pets-64 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:pcuenq/oxford-pets",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-06T14:41:29+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-pcuenq/oxford-pets #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-ema-pets-64
## Model description
This diffusion model is trained with the Diffusers library
on the 'pcuenq/oxford-pets' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: des... | [
"# ddpm-ema-pets-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'pcuenq/oxford-pets' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## ... | [
"TAGS\n#diffusers #tensorboard #en #dataset-pcuenq/oxford-pets #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-ema-pets-64",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'pcuenq/oxford-pets' dataset.",
"## Intended uses & limitations",
"##... |
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. -->
# twitter_classification
Label mapping:
```
{
'Business': 2,
'Civil Society': 4,
'Government': 5,
'Individual': 0,
'New... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "twitter_classification", "results": []}]} | JTH/twitter_classification | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T14:46:21+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# twitter_classification
Label mapping:
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# twitter_classification\n Label mapping:\n \n\nThis model is a fine-tuned version of distilbert-base-uncased 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 #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# twitter_classification\n Label mapping:\n \n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model... |
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="Taratata/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": "7.48 +/... | Taratata/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-06T15:14:02+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"
] |
token-classification | flair |
## Persian Part-of-Speech Tagging in Flair
This is the part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **??** (UPC-2017)
List of Tags in UPC:
| **tag** | **meaning** |
|:--------:|:-----------------------:|
| ADJ | adjective ... | {"language": "fa", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["UPC-2017"], "widget": [{"text": "\u062a\u0645\u0627\u0645 \u0627\u06cc\u0631\u0627\u0646 \u06cc\u06a9 \u062a\u0627\u0628\u0633\u062a\u0627\u0646 \u062a\u0646\u0648\u0631\u06cc \u0631\u0627 \u062a\u062c\u0631\u0628\u0647... | hamedkhaledi/persian-flair-pos | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fa",
"dataset:UPC-2017",
"region:us"
] | null | 2022-08-06T15:25:58+00:00 | [] | [
"fa"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fa #dataset-UPC-2017 #region-us
| Persian Part-of-Speech Tagging in Flair
---------------------------------------
This is the part-of-speech tagging model for Persian that ships with Flair.
F1-Score: ?? (UPC-2017)
List of Tags in UPC:
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following outp... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:"
] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fa #dataset-UPC-2017 #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:"
] |
summarization | transformers |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for model
| Model Name | MM Params | Inference... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | vishw2703/unisumm_3 | null | [
"transformers",
"pytorch",
"jax",
"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T15:48:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for model
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for model"
] | [
"TAGS\n#transformers #pytorch #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-arabic-saudi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-arabic-saudi-colab", "results": []}]} | bassemessam/wav2vec2-large-xls-r-300m-arabic-saudi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T18:09:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-arabic-saudi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proced... | [
"# wav2vec2-large-xls-r-300m-arabic-saudi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-arabic-saudi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.",
"## Mo... |
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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"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": ... | jackoyoungblood/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-06T18:13:22+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"
] |
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-finetuned-sst-2-english](... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | zboxi7/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 | 2022-08-06T18:21:49+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-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1460
- Accuracy: 0.75
## Model description
More information needed
## Intended uses & limitations... | [
"# finetuning-sentiment-model-3000-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: 1.1460\n- Accuracy: 0.75",
"## 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-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on... |
null | null | pip install rubika
pip install colorama
pip install rubpy | {} | Yousef7/U | null | [
"region:us"
] | null | 2022-08-06T18:43:17+00:00 | [] | [] | TAGS
#region-us
| pip install rubika
pip install colorama
pip install rubpy | [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0**
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 ...
fro... | {"library_name": "stable-baselines3", "tags": ["HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetahBulletEnv-v0... | abcp4/a2c-HalfCheetahBulletEnv-v0 | null | [
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-06T19:10:39+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing HalfCheetahBulletEnv-v0
This is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable... |
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-v0.5-e1
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bar... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-v0.5-e1", "results": []}]} | theojolliffe/bart-paraphrase-v0.5-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T19:20:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v0.5-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: 0.1769
* Rouge1: 71.0569
* Rouge2: 64.3725
* Rougel: 68.4976
* Rougelsum: 68.8318
* Gen Len: 19.3131
Model descri... | [
"### 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/1479595267800322048/Aqqb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/chai_ste/1659822641053/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/chai_ste | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T19:38:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ste
@chai\_ste
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **HopperBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HopperBulletEnv-v0**
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 huggingf... | {"library_name": "stable-baselines3", "tags": ["HopperBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HopperBulletEnv-v0", "type":... | abcp4/a2c-HopperBulletEnv-v0 | null | [
"stable-baselines3",
"HopperBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-06T20:02:10+00:00 | [] | [] | TAGS
#stable-baselines3 #HopperBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing HopperBulletEnv-v0
This is a trained model of a A2C agent playing HopperBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing HopperBulletEnv-v0\nThis is a trained model of a A2C agent playing HopperBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #HopperBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing HopperBulletEnv-v0\nThis is a trained model of a A2C agent playing HopperBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nT... |
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-v2-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-v2-e1", "results": []}]} | theojolliffe/bart-paraphrase-v2-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T20:11:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v2-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: 0.1536
* Rouge1: 72.6807
* Rouge2: 68.1957
* Rougel: 71.1787
* Rougelsum: 71.3376
* Gen Len: 19.5698
Model descriptio... | [
"### 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 the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-v4-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-v4-e1", "results": []}]} | theojolliffe/bart-paraphrase-v4-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T20:43:12+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
=====================
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: 0.1318
* Rouge1: 73.1451
* Rouge2: 69.0788
* Rougel: 71.9928
* Rougelsum: 72.1526
* Gen Len: 19.3423
Model descriptio... | [
"### 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/1537858520871149569/meL8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/xnicoleanistonx/1659822957190/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/xnicoleanistonx | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T20:52:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
URL
@xnicoleanistonx
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MT5_small_sum-de_GNAD
This model is a fine-tuned version of [Einmalumdiewelt/MT5_small_sum-de_GNAD](https://huggingface.co/Einma... | {"language": ["de"], "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "MT5_small_sum-de_GNAD", "results": []}]} | Einmalumdiewelt/MT5_small_sum-de_GNAD | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T20:56:49+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# MT5_small_sum-de_GNAD
This model is a fine-tuned version of Einmalumdiewelt/MT5_small_sum-de_GNAD on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4236
- Rouge1: 26.0749
- Rouge2: 8.4015
- Rougel: 18.7914
- Rougelsum: 22.9903
- Gen Len: 48.9027
## Model description
More ... | [
"# MT5_small_sum-de_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/MT5_small_sum-de_GNAD on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4236\n- Rouge1: 26.0749\n- Rouge2: 8.4015\n- Rougel: 18.7914\n- Rougelsum: 22.9903\n- Gen Len: 48.9027",
"## Model des... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# MT5_small_sum-de_GNAD\n\nThis model is a fine-tuned version of Einmalumdiewelt/MT5_small_sum-de_GNAD 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-paraphrase-v0.75-e1
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-v0.75-e1", "results": []}]} | theojolliffe/bart-paraphrase-v0.75-e1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-06T21:14:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-v0.75-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: 0.1865
* Rouge1: 71.3427
* Rouge2: 66.0011
* Rougel: 69.8855
* Rougelsum: 69.9796
* Gen Len: 19.6036
Model desc... | [
"### 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/540350928487202817/HM-MT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jimmie_graham/1659825365693/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jimmie_graham | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T21:33:24+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jimmie Graham
@jimmie\_graham
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | 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="jaybeeja/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": ... | jaybeeja/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-06T21:45:05+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"
] |
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/1156915847/2591287537_ba... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jimmie_graham-twittels/1659826242804/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jimmie_graham-twittels | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T21:50:02+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Harris Wittels & Jimmie Graham
@jimmie\_graham-twittels
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln62Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln62Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln62Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-06T21:58:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **CartPole-v1**
This is a trained model of a **A2C** agent playing **CartPole-v1**
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 import... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | abcp4/a2c-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-07T01:07:19+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing CartPole-v1
This is a trained model of a A2C agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
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. -->
# Fine-tuned-T5-for-MCQGenerator
This model is a fine-tuned version of [ramsrigouthamg/t5_squad_v1](https://huggingface.co/ramsrig... | {"tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "Fine-tuned-T5-for-MCQGenerator", "results": []}]} | sherwinseah/Fine-tuned-T5-for-MCQGenerator | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T02:07:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fine-tuned-T5-for-MCQGenerator
This model is a fine-tuned version of ramsrigouthamg/t5_squad_v1 on the squad_modified_for_t5_qg dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# Fine-tuned-T5-for-MCQGenerator\n\nThis model is a fine-tuned version of ramsrigouthamg/t5_squad_v1 on the squad_modified_for_t5_qg dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fine-tuned-T5-for-MCQGenerator\n\nThis model is a fine-tuned version of ramsrigouthamg/t5_squad_v... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-urdu-cv-10
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_10_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu-cv-10", "results": []}]} | omar47/wav2vec2-large-xls-r-300m-urdu-cv-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T02:22:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-urdu-cv-10
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_10\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5959
* Wer: 0.3946
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #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... |
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_3-Pretrained
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the follow... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "dna_3-Pretrained", "results": []}]} | Mozart-coder/DNA_BigBird_3 | null | [
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T02:30:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #big_bird #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| dna\_3-Pretrained
=================
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6003
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #big_bird #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: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_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-base-uncased-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned", "results": []}]} | hazrulakmal/bert-base-uncased-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T02:41:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned
===========================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4410
* Accuracy: 0.8550
* F1: 0.8557
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: 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 #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\\... |
null | null |
# Concept Art Generator
Concept Art Generator is trained on Full-Body Character Concepts, Full-Body Portraits and Half-Body Portraits the goal was to obtain better upper bodies and character style concept art, something the Portrait Generator isn't as good with.
#### Alpha Releases are Licensed under: cc-by-nc-nd-4... | {"license": "cc-by-nc-nd-4.0"} | WAS/concept_art_generator | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2022-08-07T03:01:48+00:00 | [] | [] | TAGS
#license-cc-by-nc-nd-4.0 #region-us
|
# Concept Art Generator
Concept Art Generator is trained on Full-Body Character Concepts, Full-Body Portraits and Half-Body Portraits the goal was to obtain better upper bodies and character style concept art, something the Portrait Generator isn't as good with.
#### Alpha Releases are Licensed under: cc-by-nc-nd-4... | [
"# Concept Art Generator\n\nConcept Art Generator is trained on Full-Body Character Concepts, Full-Body Portraits and Half-Body Portraits the goal was to obtain better upper bodies and character style concept art, something the Portrait Generator isn't as good with.",
"#### Alpha Releases are Licensed under: cc-b... | [
"TAGS\n#license-cc-by-nc-nd-4.0 #region-us \n",
"# Concept Art Generator\n\nConcept Art Generator is trained on Full-Body Character Concepts, Full-Body Portraits and Half-Body Portraits the goal was to obtain better upper bodies and character style concept art, something the Portrait Generator isn't as good with.... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# rwang5688/distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "rwang5688/distilbert-base-uncased-finetuned-sst2", "results": []}]} | rwang5688/distilbert-base-uncased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T03:24:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| rwang5688/distilbert-base-uncased-finetuned-sst2
================================================
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: 0.1022
* Validation Loss: 0.2643
* Train Accuracy: 0.9014
* Ep... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': Fal... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Ada... |
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="jaybeeja/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": "7.52 +/... | jaybeeja/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-07T03:27:05+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"
] |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-mask-prompt-e-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/relbert) librar... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-mask-prompt-e-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics... | research-backup/roberta-large-conceptnet-mask-prompt-e-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T03:35:51+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-mask-prompt-e-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, full resu... | [
"# relbert/roberta-large-conceptnet-mask-prompt-e-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 Question (dataset... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-mask-prompt-e-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done vi... |
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. -->
# comment-detection-prop-16
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "comment-detection-prop-16", "results": []}]} | ultra-coder54732/comment-detection-prop-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T04:37:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# comment-detection-prop-16
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 hyperparamet... | [
"# comment-detection-prop-16\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 procedure"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# comment-detection-prop-16\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMo... |
text-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. -->
# opt-350m-opty-350m-lectures
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) ... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-350m-opty-350m-lectures", "results": []}]} | pritoms/opt-350m-opty-350m-lectures | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T04:52:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| opt-350m-opty-350m-lectures
===========================
This model is a fine-tuned version of facebook/opt-350m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3830
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n... |
text-classification | transformers | ## Arabic-MARBERT-Sentiment Model
#### Model description
**Arabic-MARBERT-Sentiment Model** is a Sentiment analysis model that was built by fine-tuning the [MARBERT](https://huggingface.co/UBC-NLP/MARBERT) model. For the fine-tuning, I used [KAUST dataset](https://www.kaggle.com/competitions/arabic-sentiment-analysis-2... | {"language": ["ar"], "tags": ["text classification", "Sentiment"], "widget": [{"text": "\u0644\u0642\u062f \u0643\u0627\u0646 \u0627\u0644\u0627\u062d\u062a\u0641\u0627\u0644 \u0631\u0627\u0626\u0639"}, {"text": "\u0647\u0646\u0627\u0643 \u0628\u0639\u0636 \u0627\u0644\u0642\u0648\u0627\u0646\u064a\u0646 \u0627\u0644\u... | Ammar-alhaj-ali/arabic-MARBERT-sentiment | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text classification",
"Sentiment",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T04:53:01+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #text classification #Sentiment #ar #autotrain_compatible #endpoints_compatible #region-us
| ## Arabic-MARBERT-Sentiment Model
#### Model description
Arabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used KAUST dataset, which includes 3 labels(positive,negative,and neutral).
#### How to use
To use the model with a transformers... | [
"## Arabic-MARBERT-Sentiment Model",
"#### Model description\nArabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used KAUST dataset, which includes 3 labels(positive,negative,and neutral).",
"#### How to use\nTo use the model with... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text classification #Sentiment #ar #autotrain_compatible #endpoints_compatible #region-us \n",
"## Arabic-MARBERT-Sentiment Model",
"#### Model description\nArabic-MARBERT-Sentiment Model is a Sentiment analysis model that was built by fine-tuning the MA... |
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. -->
# stance-detection-prop-16
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "stance-detection-prop-16", "results": []}]} | ultra-coder54732/stance-detection-prop-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T05:06:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# stance-detection-prop-16
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 hyperparamete... | [
"# stance-detection-prop-16\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 procedure",... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# stance-detection-prop-16\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMor... |
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-wikiandmark_epoch60
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wikiandmark_epoch60", "results": []}]} | leokai/distilbert-base-uncased-finetuned-wikiandmark_epoch60 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T05:10:11+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-wikiandmark_epoch60
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0453
- eval_accuracy: 0.9962
- eval_runtime: 57.0954
- eval_samples_per_second: 275.364
- eval_steps_pe... | [
"# distilbert-base-uncased-finetuned-wikiandmark_epoch60\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- eval_loss: 0.0453\n- eval_accuracy: 0.9962\n- eval_runtime: 57.0954\n- eval_samples_per_second: 275.364\n- eva... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-wikiandmark_epoch60\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknow... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | btsas/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-07T05:22:07+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | btsas/Reinforce-cartpole | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-07T05:41:39+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | osanseviero/is_it_a_llama | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-08-07T06:02:02+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #has_space #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (d... |
token-classification | transformers | 한국인 이름 인식 모델
kor-bert fine-tuning 모델
자주 안쓰는 한글이름 기준으로
생성기를 만들어서, 16만개의 한글 이름을 생성 후 학습한 모델입니다.
ex) 안녕하세요. 임준영입니다. -> 안녕하세요. ***입니다.
```python
from transformers import BertTokenizerFast, BertForTokenClassification
from transformers import pipeline
model_name = 'joon09/kor-naver-ner-name'
tokenizer = BertTokenizerFa... | {} | joon09/kor-naver-ner-name | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T06:11:11+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| 한국인 이름 인식 모델
kor-bert fine-tuning 모델
자주 안쓰는 한글이름 기준으로
생성기를 만들어서, 16만개의 한글 이름을 생성 후 학습한 모델입니다.
ex) 안녕하세요. 임준영입니다. -> 안녕하세요. *입니다.
| [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | btsas/Reinforce-pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-07T07:02:41+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | osanseviero/llama_or_alpaca | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-08-07T07:23:33+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #has_space #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (d... |
text-generation | transformers |
# antwortemir/shouko04 Model | {"tags": ["conversational"]} | antwortemir/shouko04 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T07:25:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# antwortemir/shouko04 Model | [
"# antwortemir/shouko04 Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# antwortemir/shouko04 Model"
] |
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... | Mahmoud7/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-07T07:49:23+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... |
sentence-similarity | sentence-transformers |
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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](... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "MS Marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search_qa", "eli5", "snli", "multi_nli", "wikihow", "natural_qu... | ilan541/sbert_ssid | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T08:28:04+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us
| all-MiniLM-L6-v2
================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 sent... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncase... |
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-base-uncased-filtered-cuad
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingfac... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "legal-bert-base-uncased-filtered-cuad", "results": []}]} | alex-apostolo/legal-bert-base-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2022-08-07T08:36:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #license-cc-by-sa-4.0 #endpoints_compatible #region-us #has_space
| legal-bert-base-uncased-filtered-cuad
=====================================
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0259
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 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 #has_space \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_bat... |
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. -->
# benchmark-finetuned-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "benchmark-finetuned-bert", "results": []}]} | hazrulakmal/benchmark-finetuned-bert | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T10:11:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| benchmark-finetuned-bert
========================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3995
* Accuracy: 0.8479
* F1: 0.8480
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **PushBlock**
This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comp... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock"]} | mrm8488/PushBlock | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-PushBlock",
"region:us"
] | null | 2022-08-07T10:24:16+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us
|
# ppo Agent playing PushBlock
This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the train... | [
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #region-us \n",
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Docu... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-summarizer-finetuned
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-summarizer-finetuned", "results": []}]} | shamweel/mt5-small-summarizer-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T10:34:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-summarizer-finetuned
==============================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0158
* Rouge1: 17.7167
* Rouge2: 8.7443
* Rougel: 17.4487
* Rougelsum: 17.4013
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
text2text-generation | transformers |
# T5-base fine-tuned on SQuAD for **Question Generation**
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Question Generation** by just prepending the *answer* to the *context*.
## Details of T5
T... | {"language": "en", "datasets": ["squad"], "widget": [{"text": "answer: Manuel context: Manuel has created RuPERTa-base with the support of HF-Transformers and Google"}]} | mikesun112233/t5-base-finetuned-question-generation-ap | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T10:34:23+00:00 | [
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-squad #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-base fine-tuned on SQuAD for Question Generation
===================================================
Google's T5 fine-tuned on SQuAD v1.1 for Question Generation by just prepending the *answer* to the *context*.
Details of T5
-------------
The T5 model was presented in Exploring the Limits of Transfer Learning... | [
"# samples: 87599\nDataset: squad, Split: valid, # samples: 10570\n\n\nHow to load it from nlp\n\n\nCheck out more about this dataset and others in NLP Viewer\n\n\nModel fine-tuning ️\n--------------------\n\n\nThe training script is a slightly modified version of this awesome one by Suraj Patil\n\n\nHe also made ... | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #en #dataset-squad #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# samples: 87599\nDataset: squad, Split: valid, # samples: 10570\n\n\nHow to load it from nlp\n\n\nCheck out more about this data... |
null | transformers |
# BART-SLED (SLiding-Encoder and Decoder, base-sized model)
SLED models use pretrained, short-range encoder-decoder models, and apply them over
long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder
## Model description
This SLED model i... | {"language": "en", "license": "mit"} | tau/bart-base-sled | null | [
"transformers",
"tau/sled",
"en",
"arxiv:2208.00748",
"arxiv:1910.13461",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T11:02:50+00:00 | [
"2208.00748",
"1910.13461"
] | [
"en"
] | TAGS
#transformers #tau/sled #en #arxiv-2208.00748 #arxiv-1910.13461 #license-mit #endpoints_compatible #region-us
|
# BART-SLED (SLiding-Encoder and Decoder, base-sized model)
SLED models use pretrained, short-range encoder-decoder models, and apply them over
long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder
## Model description
This SLED model i... | [
"# BART-SLED (SLiding-Encoder and Decoder, base-sized model) \n\nSLED models use pretrained, short-range encoder-decoder models, and apply them over \nlong-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder",
"## Model description\n\nThis... | [
"TAGS\n#transformers #tau/sled #en #arxiv-2208.00748 #arxiv-1910.13461 #license-mit #endpoints_compatible #region-us \n",
"# BART-SLED (SLiding-Encoder and Decoder, base-sized model) \n\nSLED models use pretrained, short-range encoder-decoder models, and apply them over \nlong-text inputs by splitting the input i... |
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"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config"... | sunilkumardash9/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T11:04:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3213
- Accuracy: 0.8667
- F1: 0.8684
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3213\n- Accuracy: 0.8667\n- F1: 0.8684",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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. -->
# recipe-distil
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-distil", "results": []}]} | paola-md/recipe-distil | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T11:35:56+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-distil
=============
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3731
* Rmse: 1.8366
* Mse: 3.3731
* Mae: 1.6145
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: 15",
"### Train... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\... |
text2text-generation | transformers | # Model name
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `t5-base` model.
## Intended uses & limitations
The model is trained to generate reading comprehension-style que... | {} | mikesun112233/hugging3 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T11:50:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model name
## Model description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained 't5-base' model.
## Intended uses & limitations
The model is trained to generate reading comprehension-style que... | [
"# Model name",
"## Model description\n\nThis model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained 't5-base' model.",
"## Intended uses & limitations\n\nThe model is trained to generate reading compre... | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model name",
"## Model description\n\nThis model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a ... |
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. -->
# recipe-distilroberta-Is
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "recipe-distilroberta-Is", "results": []}]} | paola-md/RELEXset-MLM | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T12:14:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| recipe-distilroberta-Is
=======================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7427
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: 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: 25\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\... |
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... | Yuri/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 | 2022-08-07T12:23:41+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\\_... |
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="constanter/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"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": ... | constanter/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-07T12:56:28+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="constanter/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 +/... | constanter/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-07T13:00:12+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 |
# Detectron2 model
This repository hosts our trained Detectron2 model, that can detect segments from digitized books.
The following classes are supported:
- Illustration
- Illumination
The model is based on `faster_rcnn_R_50_FPN_3x` and was fine-tuned on own and manually annotated segments from digitized books. | {"license": "mit"} | dbmdz/detectron2-model | null | [
"license:mit",
"has_space",
"region:us"
] | null | 2022-08-07T13:25:43+00:00 | [] | [] | TAGS
#license-mit #has_space #region-us
|
# Detectron2 model
This repository hosts our trained Detectron2 model, that can detect segments from digitized books.
The following classes are supported:
- Illustration
- Illumination
The model is based on 'faster_rcnn_R_50_FPN_3x' and was fine-tuned on own and manually annotated segments from digitized books. | [
"# Detectron2 model\n\nThis repository hosts our trained Detectron2 model, that can detect segments from digitized books.\n\nThe following classes are supported:\n\n- Illustration\n- Illumination\n\nThe model is based on 'faster_rcnn_R_50_FPN_3x' and was fine-tuned on own and manually annotated segments from digiti... | [
"TAGS\n#license-mit #has_space #region-us \n",
"# Detectron2 model\n\nThis repository hosts our trained Detectron2 model, that can detect segments from digitized books.\n\nThe following classes are supported:\n\n- Illustration\n- Illumination\n\nThe model is based on 'faster_rcnn_R_50_FPN_3x' and was fine-tuned o... |
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/1479595267800322048/Aqqb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-chai_ste-punishedvirgo/1659883708573/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-chai_ste-punishedvirgo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T13:47:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
ste & Humongous Ape MP & radler inspector 🇵🇸🇺🇦
@apesahoy-chai\_ste-punishedvirgo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model wa... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-qa
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the squad... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-large-qa", "results": []}]} | srcocotero/bert-large-qa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-07T13:48:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-large-qa
This model is a fine-tuned version of bert-large-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 f... | [
"# bert-large-qa\n\nThis model is a fine-tuned version of bert-large-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",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-large-qa\n\nThis model is a fine-tuned version of bert-large-uncased on the squad dataset.",
"## Model description\n\nMore information nee... |
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... | cataluna84/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 | 2022-08-07T14:11:07+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 | # Produced with ⚙️ by [mimicbot](https://github.com/CakeCrusher/mimicbot)🤖 | {"tags": ["conversational"]} | SebastianS/MetalSebastian | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-07T14:25:14+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Produced with ️ by mimicbot | [
"# Produced with ️ by mimicbot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Produced with ️ by mimicbot"
] |
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