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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers | ## This is the model for the 'Sum_it' app ##
Find it at HuggingFace Spaces!
https://huggingface.co/spaces/SamuelMiller/sum_it | {} | SamuelMiller/lil_sumsum | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T05:21:08+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## This is the model for the 'Sum_it' app ##
Find it at HuggingFace Spaces!
URL | [
"## This is the model for the 'Sum_it' app ##\n\n Find it at HuggingFace Spaces!\n URL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## This is the model for the 'Sum_it' app ##\n\n Find it at HuggingFace Spaces!\n URL"
] |
text-generation | transformers |
# Zeni DialoGPT Model | {"tags": ["conversational"]} | t8oo/DialoGPT-small-zeni | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T05:52:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Zeni DialoGPT Model | [
"# Zeni DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Zeni DialoGPT Model"
] |
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": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_rewar... | Alex-yang/q-FrozenLake-v1-4x4-noSlippery | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T05:53:59+00:00 | [] | [] | TAGS
#Taxi-v3 #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#Taxi-v3 #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"
] |
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. -->
# wav2vec2-1b-npsc-nst-bokmaal
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-1b-npsc-nst-bokmaal", "results": []}]} | NbAiLab/wav2vec2-1b-npsc-nst-bokmaal | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T05:54:22+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-1b-npsc-nst-bokmaal
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0636
* Wer: 0.0345
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-1b #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... |
token-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. -->
# chanifrusydi/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on con... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "chanifrusydi/bert-finetuned-ner", "results": []}]} | chanifrusydi/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T06:04:22+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chanifrusydi/bert-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-cased on conll2003.
It achieves the following results on the evaluation set:
* Train Loss: 0.0282
* Validation Loss: 0.0539
* Epoch: 2
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
multiple-choice | 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. -->
# bertin-roberta-base-spanish-finetuned-recores3
This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bertin-roberta-base-spanish-finetuned-recores3", "results": []}]} | versae/bertin-roberta-base-spanish-finetuned-recores3 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"multiple-choice",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T06:16:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us
| bertin-roberta-base-spanish-finetuned-recores3
==============================================
This model is a fine-tuned version of bertin-project/bertin-roberta-base-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.0975
* Accuracy: 0.3884
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch... |
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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"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 +/... | Alex-yang/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T06:51:21+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text2text-generation | transformers |
# BanglaT5
This repository contains the pretrained checkpoint of the model **BanglaT5**. This is a sequence to sequence transformer model pretrained with the ["Span Corruption"]() objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLG tasks in bengali.
For finetuning o... | {"language": ["bn"], "licenses": ["cc-by-nc-sa-4.0"]} | csebuetnlp/banglat5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"bn",
"arxiv:2205.11081",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T06:51:38+00:00 | [
"2205.11081"
] | [
"bn"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #bn #arxiv-2205.11081 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| BanglaT5
========
This repository contains the pretrained checkpoint of the model BanglaT5. This is a sequence to sequence transformer model pretrained with the "Span Corruption" objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLG tasks in bengali.
For finetuning on... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #bn #arxiv-2205.11081 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Zenigata DialoGPT Model | {"tags": ["conversational"]} | t8oo/DialoGPT-small-zenigata | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T06:59:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Zenigata DialoGPT Model | [
"# Zenigata DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Zenigata DialoGPT Model"
] |
text-generation | transformers |
# GPT-2
Pretrained gpt model on Vietnamese language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-l... | {"language": "vi", "tags": ["vi", "vietnamese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["oscar"], "widget": [{"text": "Vi\u1ec7t Nam l\u00e0 qu\u1ed1c gia c\u00f3"}]} | NlpHUST/gpt2-vietnamese | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"vi",
"vietnamese",
"lm",
"nlp",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T07:04:12+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-oscar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT-2
Pretrained gpt model on Vietnamese language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
# How to use the model
~~~~
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('NlpHUST/... | [
"# GPT-2\n\nPretrained gpt model on Vietnamese language using a causal language modeling (CLM) objective. It was introduced in\nthis paper\nand first released at this page.",
"# How to use the model\n\n~~~~\nimport torch\nfrom transformers import GPT2Tokenizer, GPT2LMHeadModel\n\ntokenizer = GPT2Tokenizer.from_pr... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #vi #vietnamese #lm #nlp #dataset-oscar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT-2\n\nPretrained gpt model on Vietnamese language using a causal language modeling (CLM) objective. It was intro... |
text-generation | transformers | This model has been trained by the original authors of the paper [(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs.](https://www.semanticscholar.org/paper/COMET-ATOMIC-2020%3A-On-Symbolic-and-Neural-Knowledge-Hwang-Bhagavatula/e39503e01ebb108c6773948a24ca798cd444eb62) and has been released [her... | {"license": "afl-3.0"} | mismayil/comet-gpt2-ai2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T07:22:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #license-afl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model has been trained by the original authors of the paper (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs. and has been released here. Original codebase for training is here | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #license-afl-3.0 #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="comodoro/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": ... | comodoro/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T07:23:20+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"
] |
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. -->
# wav2vec2-8
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-8", "results": []}]} | chrisvinsen/wav2vec2-8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T07:25:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-8
==========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1169
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0006\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0006\n* train\\_batch\\_size: 3... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | roschmid/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T07:45:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0631
* Precision: 0.9207
* Recall: 0.9352
* F1: 0.9279
* Accuracy: 0.9832
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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-base-summarization
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the cnn_dailymail 3.0.0... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "t5-base-summarization", "results": []}]} | jimypbr/t5-base-test | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T08:03:54+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-summarization
This model is a fine-tuned version of t5-base on the cnn_dailymail 3.0.0 dataset.
## Model description
More information needed
## Intended uses & limitations
This is a work in progress. Please don't use these weights. :)
## Training and evaluation data
More information needed
## Train... | [
"# t5-base-summarization\n\nThis model is a fine-tuned version of t5-base on the cnn_dailymail 3.0.0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nThis is a work in progress. Please don't use these weights. :)",
"## Training and evaluation data\n\nMore inform... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-summarization\n\nThis model is a fine-tuned version of t5-base on the cnn_... |
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... | stplgg/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 | 2022-05-23T08:19:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
null | null | Kalyana Virundhu Biryani is one of the best biryani Empires in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
https://www.kalyanavirundhubiryani.com/ | {} | kalyanavirundhubiryani/good-biryani-in-chennai | null | [
"region:us"
] | null | 2022-05-23T08:32:57+00:00 | [] | [] | TAGS
#region-us
| Kalyana Virundhu Biryani is one of the best biryani Empires in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
URL | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hungarian-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hungarian-colab", "results": []}]} | hamidov02/wav2vec2-large-xls-r-300m-hungarian-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T08:39:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-hungarian-colab
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6404
* Wer: 0.4662
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
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-cnn-pubmed-arxiv-pubmed-v3-e100
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://h... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-v3-e100", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e100 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T09:06:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-v3-e100
====================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1806
* Rouge1: 59.4159
* Rouge2: 48.867
* Rougel: 51.9013
* Rougelsum: 5... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
reinforcement-learning | null |
# **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="comodoro/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": "9.59 +/... | comodoro/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T09:35:59+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"
] |
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="unfinity/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": ... | unfinity/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T09:53:01+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"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-TT2-exam
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-TT2-exam", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name":... | roschmid/distilbert-base-uncased-finetuned-TT2-exam | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T10:01:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-TT2-exam
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0620
* Precision: 0.9222
* Recall: 0.9369
* F1: 0.9295
* Accuracy: 0.9835
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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="unfinity/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": "12.00 +... | unfinity/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T10:09:19+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 | transformers |
# IndicNER
IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other publi... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": "mit", "tags": ["ner", "Pytorch", "transformer", "multilingual", "nlp", "indicnlp"], "datasets": ["Samanantar"]} | ai4bharat/IndicNER | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"ner",
"Pytorch",
"transformer",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
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"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:Samanantar",
"arxiv:2212.10168",
"license:mit",
"autotrain_compati... | null | 2022-05-23T10:12:43+00:00 | [
"2212.10168"
] | [
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te"
] | TAGS
#transformers #pytorch #bert #token-classification #ner #Pytorch #transformer #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-Samanantar #arxiv-2212.10168 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# IndicNER
IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other publi... | [
"# IndicNER\nIndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #ner #Pytorch #transformer #multilingual #nlp #indicnlp #as #bn #gu #hi #kn #ml #mr #or #pa #ta #te #dataset-Samanantar #arxiv-2212.10168 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# IndicNER\nIndicNER is a model t... |
image-segmentation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-pavement
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the reannay... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-pavement", "results": []}]} | reannayang/segformer-b0-pavement | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T10:20:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| segformer-b0-pavement
=====================
This model is a fine-tuned version of nvidia/mit-b0 on the reannayang/FL\_pavement dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4165
* Mean Iou: 0.6318
* Mean Accuracy: 0.9700
* Overall Accuracy: 0.9738
* Per Category Iou: [0.0, 0.9641663829... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 2\n... |
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. -->
# wav2vec2-9
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-9", "results": []}]} | chrisvinsen/wav2vec2-9 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T10:21:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-9
==========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0821
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.005\n* train\\_batch\\_size: 32... |
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-movie-sentiment-model-9000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-movie-sentiment-model-9000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "a... | Manishkalra/finetuning-movie-sentiment-model-9000-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 | 2022-05-23T10:33:33+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-movie-sentiment-model-9000-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: 0.4040
- Accuracy: 0.9178
- F1: 0.9155
## Model description
More information needed
## Intended uses & limitations
M... | [
"# finetuning-movie-sentiment-model-9000-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: 0.4040\n- Accuracy: 0.9178\n- F1: 0.9155",
"## Model description\n\nMore information needed",
"## Intended use... | [
"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-movie-sentiment-model-9000-samples\n\nThis model is a fine-tuned version of distilbert-base-u... |
text-generation | transformers |
# BPT2
See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = ... | {"language": "de", "widget": [{"text": "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einh\u00f6rner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."}]} | tursunali/bpt-2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T10:40:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# BPT2
See the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.
## Usage
Also, two tricks might improve the generated text:
## Citing
Please cite BPT2 as follows:
| [
"# BPT2\n\nSee the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.",
"## Usage\n\n\n\nAlso, two tricks might improve the generated text:",
"## Citing\n\nPlease cite BPT2 as follows:"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# BPT2\n\nSee the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.",
"## Usage\n\n\n\nAlso, two tricks ... |
automatic-speech-recognition | speechbrain |
# German ASR
This model is trained on the Mozilla Common Voice 8.0, the Spoken Wikipedia Corpus and the m-ailabs corpus.
In contrast to our first model this one can transcribe German Umlauts. Additionally it was trained on a newer Version of the Mozilla Common Voice.
- https://nats.gitlab.io/swc/
- https://commo... | {"language": "de", "license": "cc-by-sa-4.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "pytorch", "speechbrain"], "metrics": ["wer"]} | jfreiwa/asr-crdnn-german-umlaute | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"de",
"arxiv:2106.04624",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-05-23T10:45:45+00:00 | [
"2106.04624"
] | [
"de"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #de #arxiv-2106.04624 #license-cc-by-sa-4.0 #region-us
|
# German ASR
This model is trained on the Mozilla Common Voice 8.0, the Spoken Wikipedia Corpus and the m-ailabs corpus.
In contrast to our first model this one can transcribe German Umlauts. Additionally it was trained on a newer Version of the Mozilla Common Voice.
- URL
- URL
- URL
We do not provide a lang... | [
"# German ASR\n\nThis model is trained on the Mozilla Common Voice 8.0, the Spoken Wikipedia Corpus and the m-ailabs corpus.\nIn contrast to our first model this one can transcribe German Umlauts. Additionally it was trained on a newer Version of the Mozilla Common Voice.\n\n - URL\n - URL\n - URL\n\nWe do not p... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #de #arxiv-2106.04624 #license-cc-by-sa-4.0 #region-us \n",
"# German ASR\n\nThis model is trained on the Mozilla Common Voice 8.0, the Spoken Wikipedia Corpus and the m-ailabs corpus.\nIn contrast to our first model this one can transcrib... |
null | transformers | # Multihop-Dense Retrieval (MDR)
Paper: [Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval](https://arxiv.org/abs/2009.12756) (Xiong, Wenhan, et al.)
Code and checkpoint from https://github.com/facebookresearch/multihop_dense_retrieval/
This is the checkpoint `q_model` from https://dl.fbaipubli... | {} | deutschmann/mdr_roberta_q_encoder | null | [
"transformers",
"pytorch",
"roberta",
"arxiv:2009.12756",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T11:11:49+00:00 | [
"2009.12756"
] | [] | TAGS
#transformers #pytorch #roberta #arxiv-2009.12756 #endpoints_compatible #region-us
| # Multihop-Dense Retrieval (MDR)
Paper: Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval (Xiong, Wenhan, et al.)
Code and checkpoint from URL
This is the checkpoint 'q_model' from URL
License: URL | [
"# Multihop-Dense Retrieval (MDR) \n\nPaper: Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval (Xiong, Wenhan, et al.)\n\nCode and checkpoint from URL\n\nThis is the checkpoint 'q_model' from URL\n\nLicense: URL"
] | [
"TAGS\n#transformers #pytorch #roberta #arxiv-2009.12756 #endpoints_compatible #region-us \n",
"# Multihop-Dense Retrieval (MDR) \n\nPaper: Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval (Xiong, Wenhan, et al.)\n\nCode and checkpoint from URL\n\nThis is the checkpoint 'q_model' from URL\n\... |
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="fabiochiu/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 +/... | fabiochiu/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T11:12:47+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | mkkc58/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T11:25:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased 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
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased 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",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
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="fabiochiu/q-FrozenLake-v1-8x8-no-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-no-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type":... | fabiochiu/q-FrozenLake-v1-8x8-no-slippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T11:27:16+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-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-v1-8x8-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 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="fabiochiu/q-FrozenLake-v1-4x4-no-slippery", 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-no-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type":... | fabiochiu/q-FrozenLake-v1-4x4-no-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T11:34:16+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="fabiochiu/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | fabiochiu/q-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T11:37:41+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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 #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 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="fabiochiu/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "met... | fabiochiu/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T11:41:12+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #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-v1-8x8 #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](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", "args": ... | DuboiJ/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 | 2022-05-23T12:20:04+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: 0.3211
- Accuracy: 0.8633
- F1: 0.8638
## 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: 0.3211\n- Accuracy: 0.8633\n- F1: 0.8638",
"## 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-generation | transformers |
# BPT2
See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = ... | {"language": "de", "widget": [{"text": "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einh\u00f6rner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."}]} | tursunali/bpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T12:20:15+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# BPT2
See the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.
## Usage
Also, two tricks might improve the generated text:
## Citing
Please cite BPT2 as follows:
| [
"# BPT2\n\nSee the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.",
"## Usage\n\n\n\nAlso, two tricks might improve the generated text:",
"## Citing\n\nPlease cite BPT2 as follows:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# BPT2\n\nSee the GPT2 model card for considerations on limitations and bias. See the GPT2 documentation for details on GPT2.",
"## Usage\n\n\n\nAlso, two tricks might... |
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="unfinity/q-Taxi-v3-bug", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-bug", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "12.... | unfinity/q-Taxi-v3-bug | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T12:27:17+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"
] |
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-finetunned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetunned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotio... | fabraz/distilbert-base-uncased-finetunned-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 | 2022-05-23T12:41:47+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-finetunned-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: 0.2102
* Accuracy: 0.9285
* F1: 0.9284
Model description
-----------------
... | [
"### 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... |
summarization | transformers | # BART-base-Booksum
This is a BART-base model fine-tuned on a BookSum dataset
- **Use cases:** book summarization, general text summarization.
- This is a [`https://huggingface.co/facebook/bart-base`](https://huggingface.co/facebook/bart-base), fine-tuned for five epochs
| {"language": "en", "license": "apache-2.0", "tags": ["booksum", "summary", "summarization", "book"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "In the dead night, Frodo lay in a dream without light. Then he saw the young moon rising; under its thin light there loomed before him a black w... | KamilAin/bart-base-booksum | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"booksum",
"summary",
"summarization",
"book",
"en",
"dataset:kmfoda/booksum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T12:46:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #booksum #summary #summarization #book #en #dataset-kmfoda/booksum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # BART-base-Booksum
This is a BART-base model fine-tuned on a BookSum dataset
- Use cases: book summarization, general text summarization.
- This is a 'URL fine-tuned for five epochs
| [
"# BART-base-Booksum\n\nThis is a BART-base model fine-tuned on a BookSum dataset\n\n- Use cases: book summarization, general text summarization. \n- This is a 'URL fine-tuned for five epochs"
] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #booksum #summary #summarization #book #en #dataset-kmfoda/booksum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART-base-Booksum\n\nThis is a BART-base model fine-tuned on a BookSum dataset\n\n- Use cases... |
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. -->
# deep-haiku-gpt-2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the [haiku](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deep-haiku-gpt-2", "results": []}]} | fabianmmueller/deep-haiku-gpt-2 | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T13:28:35+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# deep-haiku-gpt-2
This model is a fine-tuned version of gpt2 on the haiku dataset.
## Model description
The model is a fine-tuned version of GPT-2 for generation of Haikus. The model, data and training procedure is inspired by a blog post by Robert A. Gonsalves. Instead of using a 8bit version of GPT-J 6B, we in... | [
"# deep-haiku-gpt-2\n\nThis model is a fine-tuned version of gpt2 on the haiku dataset.",
"## Model description\n\nThe model is a fine-tuned version of GPT-2 for generation of Haikus. The model, data and training procedure is inspired by a blog post by Robert A. Gonsalves. Instead of using a 8bit version of GPT-J... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# deep-haiku-gpt-2\n\nThis model is a fine-tuned version of gpt2 on the haiku dataset.",
"## Model description... |
text2text-generation | transformers | # dant5-small
---
language:
- da
language_bcp47:
- da
- da-bornholm
- da-synnejyl
tags:
- t5
license: cc-by-4.0
datasets:
- dagw
widget:
- text: "Aarhus er Danmarks <extra_id_0>.<extra_id_2>"
co2_eq_emissions:
training_type: "pretraining"
geographical_location: "Copenhagen, Denmark"
hardware_used: "4... | {} | strombergnlp/dant5-small | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:2208.12097",
"doi:10.57967/hf/0012",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T13:36:37+00:00 | [
"2208.12097"
] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #arxiv-2208.12097 #doi-10.57967/hf/0012 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # dant5-small
---
language:
- da
language_bcp47:
- da
- da-bornholm
- da-synnejyl
tags:
- t5
license: cc-by-4.0
datasets:
- dagw
widget:
- text: "Aarhus er Danmarks <extra_id_0>.<extra_id_2>"
co2_eq_emissions:
training_type: "pretraining"
geographical_location: "Copenhagen, Denmark"
hardware_used: "4... | [
"# dant5-small\n\n---\nlanguage:\n- da\nlanguage_bcp47:\n- da\n- da-bornholm\n- da-synnejyl\ntags:\n- t5\nlicense: cc-by-4.0\ndatasets:\n- dagw\nwidget:\n- text: \"Aarhus er Danmarks <extra_id_0>.<extra_id_2>\"\nco2_eq_emissions:\n training_type: \"pretraining\"\n geographical_location: \"Copenhagen, Denm... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #arxiv-2208.12097 #doi-10.57967/hf/0012 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# dant5-small\n\n---\nlanguage:\n- da\nlanguage_bcp47:\n- da\n- da-bornholm\n- da-synnejyl\ntags:\n- t5\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-osdg
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-osdg", "results": []}]} | peter2000/xlm-roberta-base-finetuned-osdg | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T13:41:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-osdg
===============================
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: 0.6747
* Acc: 0.8296
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-07\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-07\n* train\\_batch\\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-10
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-10", "results": []}]} | chrisvinsen/wav2vec2-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T13:52:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-10
===========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0354
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### 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: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["precision", "recall"], "model-index": [{"name": "bert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emo... | coreybrady/bert-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T13:55:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-emotion
============
This model is a fine-tuned version of distilbert-base-cased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1670
* Precision: 0.7262
* Recall: 0.7255
* Fscore: 0.7253
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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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... | stochastic/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T13:59:52+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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... | Rgl73/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 | 2022-05-23T14:15:14+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: 0.2256
* Accuracy: 0.9215
* F1: 0.9217
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... |
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... | stochastic/ppo-LunarLander-v2adssadas | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T14:24:18+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
This is an experimental model that should fix your typos and punctuation.
If you like to run your own experiments or train for a different language, have a look at [the code](https://github.com/oliverguhr/spelling).
## Model description
This is a proof of concept spelling correction model for English.
## Intended ... | {"language": ["en"], "license": "mit", "metrics": ["cer"], "widget": [{"text": "lets do a comparsion", "example_title": "1"}, {"text": "Their going to be here so0n", "example_title": "2"}, {"text": "ze shop is cloed due to covid 19", "example_title": "3"}]} | oliverguhr/spelling-correction-english-base | null | [
"transformers",
"pytorch",
"tensorboard",
"onnx",
"safetensors",
"bart",
"text2text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-23T14:26:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #onnx #safetensors #bart #text2text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This is an experimental model that should fix your typos and punctuation.
If you like to run your own experiments or train for a different language, have a look at the code.
## Model description
This is a proof of concept spelling correction model for English.
## Intended uses & limitations
This project is work i... | [
"## Model description\n\nThis is a proof of concept spelling correction model for English.",
"## Intended uses & limitations\n\nThis project is work in progress, be aware that the model can produce artefacts. \nYou can test the model using the pipeline-interface:"
] | [
"TAGS\n#transformers #pytorch #tensorboard #onnx #safetensors #bart #text2text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Model description\n\nThis is a proof of concept spelling correction model for English.",
"## Intended uses & limitations\n\nThis p... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 900329401
- CO2 Emissions (in grams): 3.807314953201688
## Validation Metrics
- Loss: 0.06255918741226196
- Rouge1: 94.0693
- Rouge2: 0.0
- RougeL: 94.0693
- RougeLsum: 94.1126
- Gen Len: 2.8528
## Usage
You can use cURL to access this mode... | {"language": "en", "tags": "autotrain", "datasets": ["jeremyccollinsmpi/autotrain-data-inference_probability_3"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.807314953201688} | jeremyccollinsmpi/autotrain-inference_probability_3-900329401 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"en",
"dataset:jeremyccollinsmpi/autotrain-data-inference_probability_3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T15:01:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-jeremyccollinsmpi/autotrain-data-inference_probability_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 900329401
- CO2 Emissions (in grams): 3.807314953201688
## Validation Metrics
- Loss: 0.06255918741226196
- Rouge1: 94.0693
- Rouge2: 0.0
- RougeL: 94.0693
- RougeLsum: 94.1126
- Gen Len: 2.8528
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 900329401\n- CO2 Emissions (in grams): 3.807314953201688",
"## Validation Metrics\n\n- Loss: 0.06255918741226196\n- Rouge1: 94.0693\n- Rouge2: 0.0\n- RougeL: 94.0693\n- RougeLsum: 94.1126\n- Gen Len: 2.8528",
"## Usage\n\nYou can use... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-jeremyccollinsmpi/autotrain-data-inference_probability_3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Mod... |
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... | LookParOf/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T15:19:53+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dbmdzHateSpeech
This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-i... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "dbmdzHateSpeech", "results": []}]} | GioReg/dbmdzHateSpeech | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T15:33:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dbmdzHateSpeech
This model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7919
- Accuracy: 0.706
- F1: 0.3524
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
"# dbmdzHateSpeech\n\nThis model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7919\n- Accuracy: 0.706\n- F1: 0.3524",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dbmdzHateSpeech\n\nThis model is a fine-tuned version of dbmdz/bert-base-italian-uncased on the None dataset.\nIt achieves the following results... |
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. -->
# layoutlmv3-er-ner
This model is a fine-tuned version of [renjithks/layoutlmv3-cord-ner](https://huggingface.co/renjithks/layoutl... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-er-ner", "results": []}]} | renjithks/layoutlmv3-er-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T15:46:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv3-er-ner
=================
This model is a fine-tuned version of renjithks/layoutlmv3-cord-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2025
* Precision: 0.6442
* Recall: 0.6761
* F1: 0.6598
* Accuracy: 0.9507
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* e... |
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. -->
# mBERTnews
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "mBERTnews", "results": []}]} | GioReg/mBERTnews | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T16:24:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBERTnews
This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1136
- Accuracy: 0.9739
- F1: 0.9732
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# mBERTnews\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1136\n- Accuracy: 0.9739\n- F1: 0.9732",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBERTnews\n\nThis model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset.\nIt achieves the following results... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-tds
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
#... | {"license": "apache-2.0", "tags": ["medium-summarization", "generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-tds", "results": []}]} | transformertroy/t5-small-finetuned-tds | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"medium-summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T16:49:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #medium-summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-tds
This model is a fine-tuned version of t5-small on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
"# t5-small-finetuned-tds\n\nThis model is a fine-tuned version of t5-small on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #medium-summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-tds\n\nThis model is a fine-tuned version of t5-small on the None data... |
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... | turnip/class-ppo-lunarlander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T17:13:12+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | 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": ... | herooooooooo/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T17:24:52+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="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"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 +/... | herooooooooo/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T17:28:00+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"
] |
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-base-timit-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-google-colab", "results": []}]} | anithapappu/wav2vec2-base-timit-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T18:00:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-google-colab
================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5506
* Wer: 0.3355
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
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... | Duuuscha/ppo-lunarlander2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T18:02:57+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | # dant5-large
---
language:
- da
language_bcp47:
- da
- da-bornholm
- da-synnejyl
tags:
- t5
license: cc-by-4.0
datasets:
- dagw
widget:
- text: "Aarhus er Danmarks <extra_id_0>.<extra_id_2>"
co2_eq_emissions:
training_type: "pretraining"
geographical_location: "Copenhagen, Denmark"
hardware_used: "4... | {} | strombergnlp/dant5-large | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"arxiv:2208.12097",
"doi:10.57967/hf/0013",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T18:03:15+00:00 | [
"2208.12097"
] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #arxiv-2208.12097 #doi-10.57967/hf/0013 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # dant5-large
---
language:
- da
language_bcp47:
- da
- da-bornholm
- da-synnejyl
tags:
- t5
license: cc-by-4.0
datasets:
- dagw
widget:
- text: "Aarhus er Danmarks <extra_id_0>.<extra_id_2>"
co2_eq_emissions:
training_type: "pretraining"
geographical_location: "Copenhagen, Denmark"
hardware_used: "4... | [
"# dant5-large\n\n---\nlanguage:\n- da\nlanguage_bcp47:\n- da\n- da-bornholm\n- da-synnejyl\ntags:\n- t5\nlicense: cc-by-4.0\ndatasets:\n- dagw\nwidget:\n- text: \"Aarhus er Danmarks <extra_id_0>.<extra_id_2>\"\nco2_eq_emissions:\n training_type: \"pretraining\"\n geographical_location: \"Copenhagen, Denm... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #arxiv-2208.12097 #doi-10.57967/hf/0013 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# dant5-large\n\n---\nlanguage:\n- da\nlanguage_bcp47:\n- da\n- da-bornholm\n- da-synnejyl\ntags:\n- t5\nlicense: cc-... |
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": []}]} | Mich/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 | 2022-05-23T18:10:05+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: 2.6059
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... |
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="stochastic/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": ... | stochastic/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T18:21:56+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 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln45")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln46 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T18:22:25+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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="stochastic/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 +/... | stochastic/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T18:30:48+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"
] |
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... | DwightGunning/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T18:40:34+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | 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="KhariotnovKK/FrozenLake_v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_s... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "FrozenLake_v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x... | KhariotnovKK/FrozenLake_v1 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T18:44:42+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"
] |
image-classification | transformers |
# electric_pole_type_classification
Classify your electric pole images R200, H, R300, Portico, Poste, Tripode | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | smc/electric_pole_type_classification | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T19:10:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# electric_pole_type_classification
Classify your electric pole images R200, H, R300, Portico, Poste, Tripode | [
"# electric_pole_type_classification\n\n\nClassify your electric pole images R200, H, R300, Portico, Poste, Tripode"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# electric_pole_type_classification\n\n\nClassify your electric pole images R200, H, R300, Portico, Poste, Tripode"
] |
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="IvanTi/q-FrozenLake-v1-4x4-Slippery_param1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery_param1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"... | IvanTi/q-FrozenLake-v1-4x4-Slippery_param1 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T19:15:59+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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 #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](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", "args": ... | LianZhang/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 | 2022-05-23T19:18:33+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: 0.3182
- Accuracy: 0.8767
- F1: 0.8754
## 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: 0.3182\n- Accuracy: 0.8767\n- F1: 0.8754",
"## 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... |
null | null |
# DistilGPT2
# test
DistilGPT2 English language model pretrained with the supervision of [GPT2](https://huggingface.co/gpt2) (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 h... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["openwebtext"]} | GuillaumeSalouHF/distilgpt2 | null | [
"exbert",
"en",
"dataset:openwebtext",
"license:apache-2.0",
"region:us"
] | null | 2022-05-23T19:22:50+00:00 | [] | [
"en"
] | TAGS
#exbert #en #dataset-openwebtext #license-apache-2.0 #region-us
|
# DistilGPT2
# test
DistilGPT2 English language model pretrained with the supervision of GPT2 (the smallest version of GPT2) on OpenWebTextCorpus, a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average... | [
"# DistilGPT2",
"# test\nDistilGPT2 English language model pretrained with the supervision of GPT2 (the smallest version of GPT2) on OpenWebTextCorpus, a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). ... | [
"TAGS\n#exbert #en #dataset-openwebtext #license-apache-2.0 #region-us \n",
"# DistilGPT2",
"# test\nDistilGPT2 English language model pretrained with the supervision of GPT2 (the smallest version of GPT2) on OpenWebTextCorpus, a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension an... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 901529445
- CO2 Emissions (in grams): 110.53225910657005
## Validation Metrics
- Loss: 0.029285535216331482
- Accuracy: 0.9897211856745716
- Precision: 0.9500373934287282
- Recall: 0.9542718349358974
- F1: 0.9521499063085572
## Usage
Yo... | {"language": "en", "tags": "autotrain", "datasets": ["ismail-lucifer011/autotrain-data-name_vsv_all"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 110.53225910657005} | ismail-lucifer011/autotrain-name_vsv_all-901529445 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain",
"en",
"dataset:ismail-lucifer011/autotrain-data-name_vsv_all",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T19:33:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-ismail-lucifer011/autotrain-data-name_vsv_all #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 901529445
- CO2 Emissions (in grams): 110.53225910657005
## Validation Metrics
- Loss: 0.029285535216331482
- Accuracy: 0.9897211856745716
- Precision: 0.9500373934287282
- Recall: 0.9542718349358974
- F1: 0.9521499063085572
## Usage
Yo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 901529445\n- CO2 Emissions (in grams): 110.53225910657005",
"## Validation Metrics\n\n- Loss: 0.029285535216331482\n- Accuracy: 0.9897211856745716\n- Precision: 0.9500373934287282\n- Recall: 0.9542718349358974\n- F1: 0.952149906308... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-ismail-lucifer011/autotrain-data-name_vsv_all #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 901529445\n- CO2 Em... |
text-generation | transformers |
# pszemraj/opt-peter-2.7B
<a href="https://colab.research.google.com/gist/pszemraj/4068382a40bbf7aab50638b062bd97a9/opt-peter-2-7b-example-csearch-generation.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [facebook/opt-2... | {"license": ["other", "apache-2.0"], "library_name": "transformers", "tags": ["generated_from_trainer", "text-generation", "OPT", "non-commercial", "dialogue", "chatbot", "ai-msgbot"], "pipeline_tag": "text-generation", "widget": [{"text": "If you could live anywhere, where would it be? peter szemraj:", "example_title"... | pszemraj/opt-peter-2.7B | null | [
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"OPT",
"non-commercial",
"dialogue",
"chatbot",
"ai-msgbot",
"base_model:facebook/opt-2.7b",
"doi:10.57967/hf/0233",
"license:other",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"... | null | 2022-05-23T19:35:16+00:00 | [] | [] | TAGS
#transformers #pytorch #opt #text-generation #generated_from_trainer #OPT #non-commercial #dialogue #chatbot #ai-msgbot #base_model-facebook/opt-2.7b #doi-10.57967/hf/0233 #license-other #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pszemraj/opt-peter-2.7B
<a href="URL
<img src="URL alt="Open In Colab"/>
</a>
This model is a fine-tuned version of facebook/opt-2.7b on about 80k WhatsApp/text messages (mine). Please use responsibly :)
Test it out on Google Colab by clicking the button above.
!chatdemo
## Model description
- Exploring to ... | [
"# pszemraj/opt-peter-2.7B\n\n <a href=\"URL\n <img src=\"URL alt=\"Open In Colab\"/>\n</a>\n\nThis model is a fine-tuned version of facebook/opt-2.7b on about 80k WhatsApp/text messages (mine). Please use responsibly :)\n\nTest it out on Google Colab by clicking the button above.\n\n!chatdemo",
"## Model descri... | [
"TAGS\n#transformers #pytorch #opt #text-generation #generated_from_trainer #OPT #non-commercial #dialogue #chatbot #ai-msgbot #base_model-facebook/opt-2.7b #doi-10.57967/hf/0233 #license-other #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# pszemraj/o... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andywedlake/test-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "andywedlake/test-finetuned-imdb", "results": []}]} | andywedlake/test-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T20:16:02+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| andywedlake/test-finetuned-imdb
===============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.7505
* Validation Loss: 2.0472
* Epoch: 9
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 0.0005, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_l... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-zs-sentence-classifier
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "bert-zs-sentence-classifier", "results": []}]} | juancavallotti/bert-zs-sentence-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T20:17:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-zs-sentence-classifier
===========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3663
* F1: 0.8483
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Trainin... | [
"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: 1e-05\n* train\\_batch\\... |
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="turnip/huggingfaceclass-qtable-FrozenLake-v1-4x4-noslip", filename="q-learning.pkl")
# Don't forget to check if you need to ad... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "huggingfaceclass-qtable-FrozenLake-v1-4x4-noslip", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_s... | turnip/huggingfaceclass-qtable-FrozenLake-v1-4x4-noslip | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T20:44:23+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="turnip/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | turnip/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T20:57:05+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 |
# viniLM-2021-from-large
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: Jun 2022
<li> Model version: 2021-distilled-from-large
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Based on [MiniLMv2 distillation](https://arxiv.org/pdf/2012.15828.pdf)... | {"language": ["eng"], "license": "apache-2.0", "tags": ["PyTorch", "tensorflow"], "thumbnail": "URL to a thumbnail used in social sharing"} | VMware/vinilm-2021-from-large | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"PyTorch",
"tensorflow",
"eng",
"arxiv:2012.15828",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T21:10:33+00:00 | [
"2012.15828"
] | [
"eng"
] | TAGS
#transformers #pytorch #bert #feature-extraction #PyTorch #tensorflow #eng #arxiv-2012.15828 #license-apache-2.0 #endpoints_compatible #region-us
|
# viniLM-2021-from-large
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: Jun 2022
<li> Model version: 2021-distilled-from-large
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Based on MiniLMv2 distillation, we have distilled vBERT-2021-large int... | [
"# viniLM-2021-from-large",
"### Model Info:\n<ul>\n<li> Authors: R&D AI Lab, VMware Inc.\n<li> Model date: Jun 2022\n<li> Model version: 2021-distilled-from-large\n<li> Model type: Pretrained language model\n<li> License: Apache 2.0\n</ul>",
"#### Motivation\nBased on MiniLMv2 distillation, we have distilled v... | [
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"# viniLM-2021-from-large",
"### Model Info:\n<ul>\n<li> Authors: R&D AI Lab, VMware Inc.\n<li> Model date: Jun 2022\n<li> Model version: 2021-distilled-f... |
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. -->
# wav2vec2-11
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-11", "results": []}]} | chrisvinsen/wav2vec2-11 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T21:25:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-11
===========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0827
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 902129475
- CO2 Emissions (in grams): 111.96508441754436
## Validation Metrics
- Loss: 0.008610366843640804
- Accuracy: 0.9970010185146537
- Precision: 0.9815740365517482
- Recall: 0.9825620993589743
- F1: 0.9820678194318264
## Usage
Yo... | {"language": "en", "tags": "autotrain", "datasets": ["ismail-lucifer011/autotrain-data-company_vs_all"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 111.96508441754436} | ismail-lucifer011/autotrain-company_vs_all-902129475 | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain",
"en",
"dataset:ismail-lucifer011/autotrain-data-company_vs_all",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T21:32:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain #en #dataset-ismail-lucifer011/autotrain-data-company_vs_all #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 902129475
- CO2 Emissions (in grams): 111.96508441754436
## Validation Metrics
- Loss: 0.008610366843640804
- Accuracy: 0.9970010185146537
- Precision: 0.9815740365517482
- Recall: 0.9825620993589743
- F1: 0.9820678194318264
## Usage
Yo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 902129475\n- CO2 Emissions (in grams): 111.96508441754436",
"## Validation Metrics\n\n- Loss: 0.008610366843640804\n- Accuracy: 0.9970010185146537\n- Precision: 0.9815740365517482\n- Recall: 0.9825620993589743\n- F1: 0.982067819431... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 902129475\n- CO2 ... |
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="turnip/huggingfaceclass-qtable-FrozenLake-v1-4x4-slip", filename="q-learning.pkl")
# Don't forget to check if you need to add ... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "huggingfaceclass-qtable-FrozenLake-v1-4x4-slip", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenL... | turnip/huggingfaceclass-qtable-FrozenLake-v1-4x4-slip | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T21:51:29+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #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-v1-8x8 #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 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="turnip/huggingfaceclass-qtable-FrozenLake-v1-8x8-slip", filename="q-learning.pkl")
# Don't forget to check if you need to add ... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "huggingfaceclass-qtable-FrozenLake-v1-8x8-slip", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenL... | turnip/huggingfaceclass-qtable-FrozenLake-v1-8x8-slip | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-23T22:13:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #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-v1-8x8 #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"
] |
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. -->
# roberta-base-biomedical-clinical-es-finetuned-ner
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-biomedical-clinical-es-finetuned-ner", "results": []}]} | asdc/roberta-base-biomedical-clinical-es-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T22:38:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-biomedical-clinical-es-finetuned-ner
=================================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1382
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 14",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_bat... |
token-classification | transformers |
# deberta-small-japanese-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-small-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-small-japanese-aozora). Every short-unit-word is tagged by [UPOS](https://universa... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/deberta-small-japanese-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-23T22:55:56+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-small-japanese-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-small-japanese-aozora. Every short-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-t... | [
"# deberta-small-japanese-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-small-japanese-aozora. Every short-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\ne... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-small-japanese-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-train... |
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/1521957986335297536/itVS... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-fchollet-steak_umm | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-23T23:03:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Steak-umm & François Chollet
@elonmusk-fchollet-steak\_umm
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, c... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "ppo", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | shivr/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-23T23:30:50+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# ppo Agent playing LunarLander-v2
This is a trained model of a ppo agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# ppo Agent playing LunarLander-v2\nThis is a trained model of a ppo agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# ppo Agent playing LunarLander-v2\nThis is a trained model of a ppo agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | jcgarciaca/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-24T00:22:08+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers | # Introduction
This model translate between Yoda-ish to English and vice versa. It makes use of the [T5-base](https://huggingface.co/t5-base) model and finetuning.
Basically it trains for 2 tasks using the same dataset. In Yoda-ish to English, trains
# Dataset
For this first version of the model I used a small sample... | {} | stephenleejm/T5_yoda_translator | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-24T00:41:29+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Introduction
This model translate between Yoda-ish to English and vice versa. It makes use of the T5-base model and finetuning.
Basically it trains for 2 tasks using the same dataset. In Yoda-ish to English, trains
# Dataset
For this first version of the model I used a small sample of 20 Yoda quotes for training. I... | [
"# Introduction\n\nThis model translate between Yoda-ish to English and vice versa. It makes use of the T5-base model and finetuning.\nBasically it trains for 2 tasks using the same dataset. In Yoda-ish to English, trains",
"# Dataset\nFor this first version of the model I used a small sample of 20 Yoda quotes fo... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Introduction\n\nThis model translate between Yoda-ish to English and vice versa. It makes use of the T5-base model and finetuning.\nBasically it trains for 2... |
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-53h-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-53h-turkish-colab", "results": []}]} | hamidov02/wav2vec2-large-xls-r-53h-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T00:48:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-53h-turkish-colab
======================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4135
* Wer: 0.3247
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 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | teppei727/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-24T00:53:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0295
* Rouge1: 17.3239
* Rouge2: 8.3252
* Rougel: 16.9877
* Rougelsum: 16.9491
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*... |
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... | Shenghao1993/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 | 2022-05-24T00:53:09+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: 0.2196
* Accuracy: 0.929
* F1: 0.9289
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# arjunpatel/distilgpt2-finetuned-pokemon-moves
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "arjunpatel/distilgpt2-finetuned-pokemon-moves", "results": []}]} | arjunpatel/distilgpt2-finetuned-pokemon-moves | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-24T01:02:50+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| arjunpatel/distilgpt2-finetuned-pokemon-moves
=============================================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.8709
* Validation Loss: 2.3512
* Epoch: 14
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optim... |
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... | Axwl/LunarLanderShip | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-24T01:41:11+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | 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="turnip/huggingfaceclass-qtable-FrozenLake-v1-8x8-slip2", filename="q-learning.pkl")
# Don't forget to check if you need to add... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "huggingfaceclass-qtable-FrozenLake-v1-8x8-slip2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "Frozen... | turnip/huggingfaceclass-qtable-FrozenLake-v1-8x8-slip2 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-24T02:05:45+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #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-v1-8x8 #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"
] |
token-classification | transformers |
# deberta-small-japanese-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-small-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-small-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://unive... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/deberta-small-japanese-luw-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T02:52:45+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-small-japanese-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-small-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer PO... | [
"# deberta-small-japanese-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-small-japanese-aozora. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-small-japanese-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-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. -->
# xlsr-wav2vec2-1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlsr-wav2vec2-1", "results": []}]} | chrisvinsen/xlsr-wav2vec2-1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T03:14:49+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-wav2vec2-1
===============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5437
* Wer: 0.4412
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_ba... |
text-generation | transformers |
# Sebastian Michaelis DialoGPT Model | {"tags": ["conversational"]} | Char135/DialoGPT-medium-sebastian | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-24T03:29:47+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sebastian Michaelis DialoGPT Model | [
"# Sebastian Michaelis DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sebastian Michaelis DialoGPT Model"
] |
fill-mask | transformers |
# deberta-base-japanese-aozora
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 36 hours 44 minutes for training. You can fine-tune `deberta-base-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japane... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-base-japanese-aozora | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T03:30:28+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-base-japanese-aozora
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 36 hours 44 minutes for training. You can fine-tune 'deberta-base-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
## Refer... | [
"# deberta-base-japanese-aozora",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 36 hours 44 minutes for training. You can fine-tune 'deberta-base-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to U... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-base-japanese-aozora",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 36 hours 44... |
null | transformers | Model preentrenado | {} | luisu0124/Prueba_tf5 | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-05-24T03:40:10+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| Model preentrenado | [] | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n"
] |
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