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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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. -->
# AzzamRadman/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "AzzamRadman/bert-finetuned-ner", "results": []}]} | AzzamRadman/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T09:48:57+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AzzamRadman/bert-finetuned-ner
==============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0266
* Validation Loss: 0.0519
* Epoch: 2
Model description
-----------------
More information neede... | [
"### 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': 2634, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test2
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-un... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test2", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test2 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T10:21:18+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test2
============================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0273
* Validation Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test4
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-un... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test4", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test4 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T10:54:15+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test4
============================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8406
* Validation Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | yogeshkulkarni/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-13T10:57:45+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pong-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{... | yogeshkulkarni/Reinforce-Pong-PLE-v0 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-13T10:57:58+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text2text-generation | transformers | ## WarmMolGenTwo
A target-specific molecule generator model which is warm started (i.e. initialized) from pretrained biochemical language models and trained on interacting protein-compound pairs, viewing targeted molecular generation as a translation task between protein and molecular languages. It was introduced in t... | {"license": "mit", "tags": ["molecule-generation", "cheminformatics", "targeted-drug-design", "biochemical-language-models"], "inference": false} | gokceuludogan/WarmMolGenTwo | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"molecule-generation",
"cheminformatics",
"targeted-drug-design",
"biochemical-language-models",
"license:mit",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-08-13T11:22:06+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #molecule-generation #cheminformatics #targeted-drug-design #biochemical-language-models #license-mit #autotrain_compatible #has_space #region-us
| ## WarmMolGenTwo
A target-specific molecule generator model which is warm started (i.e. initialized) from pretrained biochemical language models and trained on interacting protein-compound pairs, viewing targeted molecular generation as a translation task between protein and molecular languages. It was introduced in t... | [
"## WarmMolGenTwo\n\nA target-specific molecule generator model which is warm started (i.e. initialized) from pretrained biochemical language models and trained on interacting protein-compound pairs, viewing targeted molecular generation as a translation task between protein and molecular languages. It was introduc... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #molecule-generation #cheminformatics #targeted-drug-design #biochemical-language-models #license-mit #autotrain_compatible #has_space #region-us \n",
"## WarmMolGenTwo\n\nA target-specific molecule generator model which is warm started (i.e. in... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test5
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-un... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test5", "results": []}]} | mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test5 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T11:40:03+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mojtaba767/bert-base-parsbert-uncased-finetuned-imdb-m-test5
============================================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.3331
* Validation Lo... | [
"### 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': 0.01, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_lea... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp... |
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": ... | xusysh/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T11:51:23+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"
] |
text-generation | transformers |
## ChemBERTaLM
A molecule generator model finetuned from [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoint. It was introduced in the paper, "Exploiting pretrained biochemical language models for
targeted drug design", which has been accepted for publication in *Bioinformatics* Publishe... | {"license": "mit", "tags": ["molecule-generation", "cheminformatics", "biochemical-language-models"], "widget": [{"text": "c1ccc2c(c1)", "example_title": "Scaffold Hopping"}]} | gokceuludogan/ChemBERTaLM | null | [
"transformers",
"pytorch",
"roberta",
"text-generation",
"molecule-generation",
"cheminformatics",
"biochemical-language-models",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-13T11:59:45+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-generation #molecule-generation #cheminformatics #biochemical-language-models #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## ChemBERTaLM
A molecule generator model finetuned from ChemBERTa checkpoint. It was introduced in the paper, "Exploiting pretrained biochemical language models for
targeted drug design", which has been accepted for publication in *Bioinformatics* Published by Oxford University Press and first released in this repos... | [
"## ChemBERTaLM\n\nA molecule generator model finetuned from ChemBERTa checkpoint. It was introduced in the paper, \"Exploiting pretrained biochemical language models for\ntargeted drug design\", which has been accepted for publication in *Bioinformatics* Published by Oxford University Press and first released in t... | [
"TAGS\n#transformers #pytorch #roberta #text-generation #molecule-generation #cheminformatics #biochemical-language-models #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## ChemBERTaLM\n\nA molecule generator model finetuned from ChemBERTa checkpoint. It was introduced in the... |
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. -->
# camembert-base-finetuned-mixte-symbole-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camember... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-mixte-symbole-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-mixte-symbole-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T12:08:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-mixte-symbole-dd
=========================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3576
* Precision: 0.8788
* Recall: 0.8986
* F1: 0.8886
* Accuracy: 0.9230
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
translation | transformers |
## About the model
It is a Turkish bert-based model created to determine the types of bullying that people use against each other in social media.
Included classes;
- Nötr
- Kızdırma/Hakaret
- Cinsiyetçilik
- Irkçılık
3388 tweets were used in the training of the model. Accordingly, the success rates in education are... | {"language": ["tr"], "license": "mit", "tags": ["translation"]} | nanelimon/bert-base-turkish-bullying | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"translation",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T12:24:56+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #text-classification #translation #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| About the model
---------------
It is a Turkish bert-based model created to determine the types of bullying that people use against each other in social media.
Included classes;
* Nötr
* Kızdırma/Hakaret
* Cinsiyetçilik
* Irkçılık
3388 tweets were used in the training of the model. Accordingly, the success rates ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #translation #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | The T5-base model was trained on cnn dailymail dataset and then fine tuned on dataset containing 11k articles.
The data fields were:
- Headlines
- ArticleBody
| {} | AkashKhamkar/T5-base-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T12:35:44+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The T5-base model was trained on cnn dailymail dataset and then fine tuned on dataset containing 11k articles.
The data fields were:
- Headlines
- ArticleBody
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Albedo DialoGPT Model | {"tags": ["conversational"]} | AlbedoAI/DialoGPT-large-Albedo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T12:53:10+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Albedo DialoGPT Model | [
"# Albedo DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Albedo DialoGPT Model"
] |
tabular-classification | sklearn |
### Load the data
```python
from datasets import load_dataset
import imodels
import numpy as np
from sklearn.model_selection import GridSearchCV
import joblib
dataset = load_dataset("imodels/compas-recidivism")
df = pd.DataFrame(dataset['train'])
X_train = df.drop(columns=['is_recid'])
y_train = df['is_recid'].valu... | {"license": "mit", "tags": ["tabular-classification", "sklearn", "imodels"], "datasets": ["imodels/compas-recidivism"], "widget": {"structuredData": {"age": [40.0, 25.0, 36.0, 23.0, 29.0], "priors_count": [0.0, 1.0, 11.0, 1.0, 0.0], "days_b_screening_arrest": [-1.0, -1.0, -1.0, -1.0, 0.0], "c_jail_time": [0.0, 1.0, 2.0... | imodels/figs-compas-recidivism | null | [
"sklearn",
"joblib",
"tabular-classification",
"imodels",
"dataset:imodels/compas-recidivism",
"license:mit",
"region:us"
] | null | 2022-08-13T12:58:17+00:00 | [] | [] | TAGS
#sklearn #joblib #tabular-classification #imodels #dataset-imodels/compas-recidivism #license-mit #region-us
|
### Load the data
### Load the model
### Make prediction
| [
"### Load the data",
"### Load the model",
"### Make prediction"
] | [
"TAGS\n#sklearn #joblib #tabular-classification #imodels #dataset-imodels/compas-recidivism #license-mit #region-us \n",
"### Load the data",
"### Load the model",
"### Make prediction"
] |
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="ekwilibrator/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | ekwilibrator/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T13:19:24+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"
] |
sentence-similarity | sentence-transformers |
# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian-obsidian
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transfo... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian-obsidian | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T13:20:54+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian-obsidian
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentenc... | [
"# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian-obsidian\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you ha... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian-obsidian\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and ... |
text-generation | transformers |
# Albedo DialoGPT Model | {"tags": ["conversational"]} | AlbedoAI/DialoGPT-large-Albedo2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T13:23:26+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Albedo DialoGPT Model | [
"# Albedo DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Albedo DialoGPT Model"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_yc_recipe_30
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_base_yc_recipe_30", "results": []}]} | CennetOguz/bert_base_yc_recipe_30 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T13:29:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_base\_yc\_recipe\_30
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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="ekwilibrator/q-Taxi-v3", 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", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | ekwilibrator/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T13:42:42+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"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_large_yc_recipe_30
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_large_yc_recipe_30", "results": []}]} | CennetOguz/bert_large_yc_recipe_30 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T13:45:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_large\_yc\_recipe\_30
===========================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | RathodSankul/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T13:49:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6561
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | rangacharysrinivasan/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T13:51:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1547
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased-finetuned-squad
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-case... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xlnet-base-cased-finetuned-squad", "results": []}]} | srikanthkb/xlnet-base-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T14:05:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| xlnet-base-cased-finetuned-squad
================================
This model is a fine-tuned version of xlnet-base-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2533
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlnet #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* e... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# UCF_Crime
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "UCF_Crime", "results": []}]} | csr2000/UCF_Crime | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T14:43:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# UCF_Crime
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# UCF_Crime\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder 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 #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# UCF_Crime\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.",
"## M... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | eugeneware/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-13T14:45:43+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
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_emo_classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the e... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "bert_emo_classifier", "results": []}]} | Manirathinam21/bert_emo_classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T14:50:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_emo\_classifier
=====================
This model is a fine-tuned version of bert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3768
Target Labels
-------------
label: a classification label, with possible values including
* sadness : 0
* joy : 1
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# VanessaSchenkel/padrao-unicamp-finetuned-opus_books
This model is a fine-tuned version of [unicamp-dl/translation-en-pt-t5](https://hu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "VanessaSchenkel/padrao-unicamp-finetuned-opus_books", "results": []}]} | VanessaSchenkel/padrao-unicamp-finetuned-opus_books | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T14:58:27+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| VanessaSchenkel/padrao-unicamp-finetuned-opus\_books
====================================================
This model is a fine-tuned version of unicamp-dl/translation-en-pt-t5 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.2766
* Validation Loss: 2.0044
* Train Bleu:... | [
"### 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 #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDe... |
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": []}]} | chou/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T15:22:34+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: 2.4453
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... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1252447945
- CO2 Emissions (in grams): 0.8697
## Validation Metrics
- Loss: 0.188
- Accuracy: 0.925
- Precision: 0.926
- Recall: 0.924
- AUC: 0.979
- F1: 0.925
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["zh"], "tags": ["autotrain", "text-classification"], "datasets": ["yuan1729/autotrain-data-Law-0_"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.8697328559727794}} | 0x-YuAN/CL_or_not | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain",
"zh",
"dataset:yuan1729/autotrain-data-Law-0_",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T15:37:10+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #electra #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-Law-0_ #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1252447945
- CO2 Emissions (in grams): 0.8697
## Validation Metrics
- Loss: 0.188
- Accuracy: 0.925
- Precision: 0.926
- Recall: 0.924
- AUC: 0.979
- F1: 0.925
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1252447945\n- CO2 Emissions (in grams): 0.8697",
"## Validation Metrics\n\n- Loss: 0.188\n- Accuracy: 0.925\n- Precision: 0.926\n- Recall: 0.924\n- AUC: 0.979\n- F1: 0.925",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #electra #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-Law-0_ #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1252447945\n- CO2 Emissions (in gr... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "imagefolder", "metrics": []} | ryu2/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-13T15:46:19+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-imagefolder #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'imagefolder' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: descri... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'imagefolder' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Tra... | [
"TAGS\n#diffusers #tensorboard #en #dataset-imagefolder #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'imagefolder' dataset.",
"## Intended uses & limitations",
"#### How to ... |
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/1554116329678016514/jkv2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/amber08007635/1660410175283/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/amber08007635 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T16:01:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Floofy
@amber08007635
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | adil-o/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-13T16:19:51+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/m... | {"license": "other", "tags": ["vision", "semantic-segmentation", "generated_from_trainer"], "datasets": ["ds_tag1", "ds_tag2"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-2", "results": []}]} | pablojrios/segformer-b0-finetuned-segments-sidewalk-2 | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"semantic-segmentation",
"generated_from_trainer",
"dataset:ds_tag1",
"dataset:ds_tag2",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T16:31:02+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #semantic-segmentation #generated_from_trainer #dataset-ds_tag1 #dataset-ds_tag2 #license-other #endpoints_compatible #region-us
| segformer-b0-finetuned-segments-sidewalk-2
==========================================
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5766
* Mean Iou: 0.1371
* Mean Accuracy: 0.1845
* Overall Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #segformer #vision #semantic-segmentation #generated_from_trainer #dataset-ds_tag1 #dataset-ds_tag2 #license-other #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\... |
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/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-pornhub/1660414195623/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-pornhub | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T17:08:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Pornhub
@elonmusk-pornhub
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Trainin... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert-base-finetuned-Train_RAW_157080-dd
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camem... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "camembert-base-finetuned-Train_RAW_157080-dd", "results": []}]} | ZhiyuanQiu/camembert-base-finetuned-Train_RAW_157080-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T17:09:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-Train\_RAW\_157080-dd
==============================================
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2610
* Precision: 0.8933
* Recall: 0.9183
* F1: 0.9056
* Accuracy: 0.9375
Mod... | [
"### 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_... |
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... | slarionne/ppo-Lander_test | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-13T17:32:36+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="ntinosmg/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", "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", "type": "FrozenLake-v1-4x4"}, "m... | ntinosmg/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T17:33:02+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"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo-colab", "results": []}]} | Trifon/wav2vec2-large-xlsr-53-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T17:51:16+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-xlsr-53-demo-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: 0.4253
* Wer: 0.4880
Model description
-----------------
More information neede... | [
"### 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... |
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. -->
# bertbasecasedfinancialphrasebank
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bertbasecasedfinancialphrasebank", "results": []}]} | ozioh/bertbasecasedfinancialphrasebank | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T17:54:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bertbasecasedfinancialphrasebank
================================
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: 0.5046
* Accuracy: 0.8660
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
reinforcement-learning | 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="ntinosmg/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": "6.00 +/... | ntinosmg/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-13T18:00:02+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers | # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
b... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["Confidential"]} | CK42/sentiment_analysis_sbcBI | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"exbert",
"en",
"dataset:Confidential",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T18:05:11+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #exbert #en #dataset-Confidential #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
## Model description
BERT is a transformers model... | [
"# BERT base model (uncased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.",
"## Model description\n\nBERT is a tr... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #exbert #en #dataset-Confidential #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base model (uncased)\n\nPretrained model on English language using a masked language modeling (MLM) objecti... |
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. -->
# nba_pbp_distilgpt2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on text files containin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "1 11:45 0-0 K. Looney misses 2-pt layup from 4 ft (block by R. Williams)", "example_title": "GSW-BOS"}], "model-index": [{"name": "nba_pbp_distilgpt2", "results": []}]} | arvkevi/nba_pbp_distilgpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T18:07:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# nba_pbp_distilgpt2
This model is a fine-tuned version of distilgpt2 on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.
It achieves the following results on the evaluation set:
- Loss: 0.6324
- Accuracy: 0.8117
## Mode... | [
"# nba_pbp_distilgpt2\n\nThis model is a fine-tuned version of distilgpt2 on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season.\n\nIt achieves the following results on the evaluation set:\n- Loss: 0.6324\n- Accuracy: 0.8117"... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# nba_pbp_distilgpt2\n\nThis model is a fine-tuned version of distilgpt2 on text files containing play-by-play descr... |
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/1535398340963094529/8lG2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/markythefluffy/1660420439548/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/markythefluffy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T18:52:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Marky
@markythefluffy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | rebolforces/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-13T20:43:12+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.fr", "s... | Keneston/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T21:03:47+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2763
* F1: 0.8346
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.it", "s... | Keneston/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T21:19:49+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2630
* F1: 0.8124
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
null | null |
tags:
- Conversational
| {} | residentalien/test | null | [
"region:us"
] | null | 2022-08-13T21:26:57+00:00 | [] | [] | TAGS
#region-us
|
tags:
- Conversational
| [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln65Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln65Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln65Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T21:31:42+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.en", "s... | Keneston/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T21:33:19+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4043
* F1: 0.6886
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
null | 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-b4-finetuned-segments-sidewalk
This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit... | {"license": "other", "tags": ["vision", "semantic-segmentation", "generated_from_trainer"], "datasets": ["ds_tag1", "ds_tag2"], "model-index": [{"name": "segformer-b4-finetuned-segments-sidewalk", "results": []}]} | pablojrios/segformer-b4-finetuned-segments-sidewalk | null | [
"transformers",
"pytorch",
"segformer",
"vision",
"semantic-segmentation",
"generated_from_trainer",
"dataset:ds_tag1",
"dataset:ds_tag2",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T21:45:57+00:00 | [] | [] | TAGS
#transformers #pytorch #segformer #vision #semantic-segmentation #generated_from_trainer #dataset-ds_tag1 #dataset-ds_tag2 #license-other #endpoints_compatible #region-us
| segformer-b4-finetuned-segments-sidewalk
========================================
This model is a fine-tuned version of nvidia/mit-b4 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6675
* Mean Iou: 0.4470
* Mean Accuracy: 0.5318
* Overall Accuracy: 0.88... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: 30",
"### Trainin... | [
"TAGS\n#transformers #pytorch #segformer #vision #semantic-segmentation #generated_from_trainer #dataset-ds_tag1 #dataset-ds_tag2 #license-other #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\... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln66Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln66Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln66Paraphrase | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-13T22:45:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
# Custom Training with YOLOv7 🔥
## Some Important links
- [Model Inference🤖](https://huggingface.co/spaces/owaiskha9654/Custom_Yolov7)
- [**🚀Training Yolov7 on Kaggle**](https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset)
- [Weight and Biases 🐝](https://wandb.ai/owaiskhan9515/YO... | {"tags": ["V20"], "metrics": ["mAP_0.5:0.95", "mAP_0.5"]} | owaiskha9654/Yolov7_Custom_Object_Detection | null | [
"V20",
"has_space",
"region:us"
] | null | 2022-08-13T22:50:29+00:00 | [] | [] | TAGS
#V20 #has_space #region-us
|
# Custom Training with YOLOv7
## Some Important links
- Model Inference
- Training Yolov7 on Kaggle
- Weight and Biases
- HuggingFace Model Repo
## Contact Information
- Name - Owais Ahmad
- Phone - +91-9515884381
- Email - owaiskhan9654@URL
- Portfolio - URL
# Objective
## To Showcase custom Object Detecti... | [
"# Custom Training with YOLOv7",
"## Some Important links\n- Model Inference\n- Training Yolov7 on Kaggle\n- Weight and Biases \n- HuggingFace Model Repo",
"## Contact Information\n\n\n- Name - Owais Ahmad\n- Phone - +91-9515884381\n- Email - owaiskhan9654@URL\n- Portfolio - URL",
"# Objective",
"## To Sho... | [
"TAGS\n#V20 #has_space #region-us \n",
"# Custom Training with YOLOv7",
"## Some Important links\n- Model Inference\n- Training Yolov7 on Kaggle\n- Weight and Biases \n- HuggingFace Model Repo",
"## Contact Information\n\n\n- Name - Owais Ahmad\n- Phone - +91-9515884381\n- Email - owaiskhan9654@URL\n- Portfo... |
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-small-finetuned-cuad-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert-small-finetuned-cuad-longer", "results": []}]} | muhtasham/bert-small-finetuned-cuad-longer | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T22:59:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-cuad #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #endpoints_compatible #region-us
| bert-small-finetuned-cuad-longer
================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0617
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-cuad #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-legal-definitions
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8]... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["finiteautomata/legal-definitions"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-legal-definitions", "results": []}]} | muhtasham/bert-small-finetuned-legal-definitions | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:finiteautomata/legal-definitions",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-13T23:38:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-finiteautomata/legal-definitions #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-legal-definitions
==========================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the legal-definitions dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5162
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-finiteautomata/legal-definitions #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamete... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-for-kaggle-mayo-clinic
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/goog... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-for-kaggle-mayo-clinic", "results": []}]} | diegopetrola/vit-for-kaggle-mayo-clinic | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T00:01:52+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| vit-for-kaggle-mayo-clinic
==========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5538
* Accuracy: 0.7616
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\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: cosine\n* lr\\_scheduler\\_warmup\\_rati... | [
"TAGS\n#transformers #pytorch #vit #image-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: 0.0003\n* train\\_batch\\_size: 64\n*... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-legal-definitions-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-5... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["finiteautomata/legal-definitions"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-legal-definitions-longer", "results": []}]} | muhtasham/bert-small-finetuned-legal-definitions-longer | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:finiteautomata/legal-definitions",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T00:18:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-finiteautomata/legal-definitions #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-legal-definitions-longer
=================================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the legal-definitions dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3701
... | [
"### 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: 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-finiteautomata/legal-definitions #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following ... |
text-generation | transformers | *** I recommend trying my other models. This one does not work well.
Trained on https://huggingface.co/uw-hai/polyjuice
Can find a version of the trained dataset here: https://github.com/BigSalmon2/InformalToFormalDataset
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.fro... | {} | BigSalmon/SmallerGPT2InformalToFormalLincoln67Paraphrase | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T00:57:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| * I recommend trying my other models. This one does not work well.
Trained on URL
Can find a version of the trained dataset here: URL
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Mas... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1107777212835614720/g_Kw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ianflynnbkc/1668480615006/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ianflynnbkc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T01:48:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ian Flynn
@ianflynnbkc
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | willmay/DialoGPT-medium-will | null | [
"conversational",
"region:us"
] | null | 2022-08-14T02:18:08+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
null | transformers | 
| Language | version ... | {} | LTP/tiny | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T03:13:03+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| !CODE SIZE
!CONTRIBUTORS
!LAST COMMIT
LTP 4
=====
LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。
引用
--
如果您在工作中使用了 LTP，您可以引用这篇论文
参考书：
由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《自然语言处理：基于预训练模型的方法
》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分... | [
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存）\n\t+ [其他改进] 改进了模型训练方法\n\t\t- [共同] 提供了训练... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6... |
null | transformers | 
| Language | version ... | {} | LTP/small | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T03:14:58+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| !CODE SIZE
!CONTRIBUTORS
!LAST COMMIT
LTP 4
=====
LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。
引用
--
如果您在工作中使用了 LTP，您可以引用这篇论文
参考书：
由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《自然语言处理：基于预训练模型的方法
》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分... | [
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存）\n\t+ [其他改进] 改进了模型训练方法\n\t\t- [共同] 提供了训练... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6... |
null | transformers | 
| Language | version ... | {} | LTP/base | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T03:15:28+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| !CODE SIZE
!CONTRIBUTORS
!LAST COMMIT
LTP 4
=====
LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。
引用
--
如果您在工作中使用了 LTP，您可以引用这篇论文
参考书：
由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《自然语言处理：基于预训练模型的方法
》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分... | [
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存）\n\t+ [其他改进] 改进了模型训练方法\n\t\t- [共同] 提供了训练... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6... |
null | transformers | 
| Language | version ... | {} | LTP/base1 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T03:35:09+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| !CODE SIZE
!CONTRIBUTORS
!LAST COMMIT
LTP 4
=====
LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。
引用
--
如果您在工作中使用了 LTP，您可以引用这篇论文
参考书：
由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《自然语言处理：基于预训练模型的方法
》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分... | [
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存）\n\t+ [其他改进] 改进了模型训练方法\n\t\t- [共同] 提供了训练... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6... |
null | transformers | 
| Language | version ... | {} | LTP/base2 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T03:36:19+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| !CODE SIZE
!CONTRIBUTORS
!LAST COMMIT
LTP 4
=====
LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。
引用
--
如果您在工作中使用了 LTP，您可以引用这篇论文
参考书：
由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《自然语言处理：基于预训练模型的方法
》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分... | [
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存）\n\t+ [其他改进] 改进了模型训练方法\n\t\t- [共同] 提供了训练... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6... |
text-generation | transformers |
# Albedo Medium DialoGPT Model Casual
This model does not do well with short greetings. But it can handle question and answer types of conversations most of the time.
It is trained on Albedo's dialogues from his story quests [Princeps Cretaceus Chapter](https://genshin-impact.fandom.com/wiki/Princeps_Cretaceus_Chapt... | {"tags": ["conversational"]} | AlbedoAI/DialoGPT-medium-Albedo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T03:44:07+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Albedo Medium DialoGPT Model Casual
This model does not do well with short greetings. But it can handle question and answer types of conversations most of the time.
It is trained on Albedo's dialogues from his story quests Princeps Cretaceus Chapter and Shadows Amidst Snowstorms Event Story
Socials
- Twitter: @to... | [
"# Albedo Medium DialoGPT Model Casual\n\nThis model does not do well with short greetings. But it can handle question and answer types of conversations most of the time.\n\nIt is trained on Albedo's dialogues from his story quests Princeps Cretaceus Chapter and Shadows Amidst Snowstorms Event Story\n\nSocials\n- T... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Albedo Medium DialoGPT Model Casual\n\nThis model does not do well with short greetings. But it can handle question and answer types of conversations most ... |
text2text-generation | transformers | ---
language:
- en
tags:
- summarization
- t5-large-xsum-cnn-8-2
- pipeline:summarization
license: mit
model-index:
- name: sysresearch101/t5-large-xsum-cnn-8-2"
results:
- task:
type: summarization
name: Summarization
dataset:
name: xsum & Cnn
type: xsum & Cnn
config: 3.0.0
... | {"model-index": [{"name": "sysresearch101/t5-large-xsum-cnn-8-2", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "xsum & Cnn", "type": "xsum & Cnn", "config": "3.0.0", "split": "train"}, "metrics": [{"type": "rouge", "value": "<TODO>", "name": "ROUGE-1", "verified": true, "... | sysresearch101/t5-large-xsum-cnn-8-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T03:58:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
language:
- en
tags:
- summarization
- t5-large-xsum-cnn-8-2
- pipeline:summarization
license: mit
model-index:
- name: sysresearch101/t5-large-xsum-cnn-8-2"
results:
- task:
type: summarization
name: Summarization
dataset:
name: xsum & Cnn
type: xsum & Cnn
config: 3.0.0
... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | 
| Language | version ... | {} | LTP/legacy | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T04:05:24+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| !CODE SIZE
!CONTRIBUTORS
!LAST COMMIT
LTP 4
=====
LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。
引用
--
如果您在工作中使用了 LTP，您可以引用这篇论文
参考书：
由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《自然语言处理：基于预训练模型的方法
》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分... | [
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存）\n\t+ [其他改进] 改进了模型训练方法\n\t\t- [共同] 提供了训练... | [
"TAGS\n#transformers #endpoints_compatible #region-us \n",
"### 更新说明\n\n\n* 4.2.0\n\t+ [结构性变化] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰\n\t\t- [Legacy 模型] 针对广大用户对于推理速度的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 3.55 倍，开启多线程更可获得 17.17 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务\n\t\t- [深度学习模型] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | WasuratS/dqn-SpaceInvaderNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-14T04:55:36+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
fill-mask | transformers | Roberta Urdu trained on urmono dataset for masked language modeling.
| {"language": ["ur"], "license": "mit", "widget": [{"text": "\u0645\u06cc\u0631\u06d2 <mask> \u06a9\u0627 \u0627\u06cc \u0645\u06cc\u0644 \u0622\u06cc\u0627\u06c1\u06d2"}]} | hadidev/robertaurdu | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ur",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T04:58:39+00:00 | [] | [
"ur"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ur #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Roberta Urdu trained on urmono dataset for masked language modeling.
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ur #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | nokomoro3/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T05:00:56+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: 0.2264
* Accuracy: 0.921
* F1: 0.9210
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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | thientran/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T06:35:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1193
* Precision: 0.8333
* Recall: 0.9322
* F1: 0.8800
* Accuracy: 0.9725
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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/1396083058844045319/d_xN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/palestinepound/1660463113168/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/palestinepound | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T06:43:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Palestine Pound
@palestinepound
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# bert-tiny-finetuned-legal-contracts-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/google/bert_uncased_L-4_H-512_A-8) on the portion of legal_contracts dataset.
# Note
The model was not trained on the whole dataset which is around 9.5 GB, but only
... | {"datasets": ["albertvillanova/legal_contracts"], "base_model": "google/bert_uncased_L-4_H-512_A-8"} | muhtasham/bert-small-finetuned-legal-contracts10train10val | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"dataset:albertvillanova/legal_contracts",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-14T07:54:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #dataset-albertvillanova/legal_contracts #base_model-google/bert_uncased_L-4_H-512_A-8 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bert-tiny-finetuned-legal-contracts-longer
This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the portion of legal_contracts dataset.
# Note
The model was not trained on the whole dataset which is around 9.5 GB, but only
## The first 10% of 'train' + the last 10% of 'train'.
| [
"# bert-tiny-finetuned-legal-contracts-longer\n\nThis model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the portion of legal_contracts dataset.",
"# Note \nThe model was not trained on the whole dataset which is around 9.5 GB, but only",
"## The first 10% of 'train' + the last 10% of 'train'... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #dataset-albertvillanova/legal_contracts #base_model-google/bert_uncased_L-4_H-512_A-8 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bert-tiny-finetuned-legal-contracts-longer\n\nThis model is a fine-tuned version of google/be... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# benchmark-finetuned-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "benchmark-finetuned-distilbert", "results": []}]} | hazrulakmal/benchmark-finetuned-distilbert | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T08:05:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| benchmark-finetuned-distilbert
==============================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4592
* Accuracy: 0.8228
* F1: 0.8214
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["eoir_privacy"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "eoir_privacy"... | muhtasham/bert-small-finetuned-eoir_privacy | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:eoir_privacy",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T08:14:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-eoir\_privacy
======================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the eoir\_privacy dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2159
* Accuracy: 0.9175
* F1: 0.8... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-cnn-adam8bit-bs4x64acc_2
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn-adam8bit-bs4x64acc_2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_daily... | oMateos2020/pegasus-newsroom-cnn-adam8bit-bs4x64acc_2 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T08:34:47+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-newsroom-cnn-adam8bit-bs4x64acc\_2
==========================================
This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn-adam8bit-bs16x64acc on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8608
* Rouge1: 44.2848
* Rouge2: 21.5452
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | yoyoyo1118/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T09:04:20+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: 0.2230
* Accuracy: 0.925
* F1: 0.9248
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... |
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="jasheershihab/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | jasheershihab/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-14T09:18:10+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1256348072
- CO2 Emissions (in grams): 8.6679
## Validation Metrics
- Loss: 0.065
- Accuracy: 0.986
- Macro F1: 0.972
- Micro F1: 0.986
- Weighted F1: 0.986
- Macro Precision: 0.973
- Micro Precision: 0.986
- Weighted Precision: ... | {"language": ["zh"], "tags": ["autotrain", "text-classification"], "datasets": ["yuan1729/autotrain-data-laws_1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 8.667918502534315}} | 0x-YuAN/Non_CL | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"zh",
"dataset:yuan1729/autotrain-data-laws_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T09:37:19+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-laws_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1256348072
- CO2 Emissions (in grams): 8.6679
## Validation Metrics
- Loss: 0.065
- Accuracy: 0.986
- Macro F1: 0.972
- Micro F1: 0.986
- Weighted F1: 0.986
- Macro Precision: 0.973
- Micro Precision: 0.986
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1256348072\n- CO2 Emissions (in grams): 8.6679",
"## Validation Metrics\n\n- Loss: 0.065\n- Accuracy: 0.986\n- Macro F1: 0.972\n- Micro F1: 0.986\n- Weighted F1: 0.986\n- Macro Precision: 0.973\n- Micro Precision: 0.986\n-... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-laws_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1256348072\n- CO2 Emissions (in ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config"... | riddhi17pawar/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T10:03:53+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: 0.3029
- Accuracy: 0.8667
- F1: 0.8675
## 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: 0.3029\n- Accuracy: 0.8667\n- F1: 0.8675",
"## 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... |
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. -->
# v7-fine-tune-wav2vec2-Vietnamese-ARS-demo
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](h... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "v7-fine-tune-wav2vec2-Vietnamese-ARS-demo", "results": []}]} | thocheat/v7-fine-tune-wav2vec2-Vietnamese-ARS-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T10:50:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| v7-fine-tune-wav2vec2-Vietnamese-ARS-demo
=========================================
This model is a fine-tuned version of nguyenvulebinh/wav2vec2-base-vietnamese-250h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2806
* Wer: 0.2751
Model description
-----------------
Mo... | [
"### 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-cc-by-nc-4.0 #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:... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-korean-demo3
This model is a fine-tuned version of [NX2411/wav2vec2-large-xlsr-korean-demo-no-LM](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["zeroth_korean"], "model-index": [{"name": "wav2vec2-large-xlsr-korean-demo3", "results": []}]} | NX2411/wav2vec2-large-xlsr-korean-demo3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:zeroth_korean",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T10:56:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-zeroth_korean #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-korean-demo3
================================
This model is a fine-tuned version of NX2411/wav2vec2-large-xlsr-korean-demo-no-LM on the zeroth\_korean dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8265
* Wer: 0.5090
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-zeroth_korean #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* ... |
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. -->
# es_financial
This model is a fine-tuned version of [huranokuma/es2](https://huggingface.co/huranokuma/es2) on an unknown dataset... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "es_financial", "results": []}]} | huranokuma/es_financial | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T12:42:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# es_financial
This model is a fine-tuned version of huranokuma/es2 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4057
- Accuracy: 0.9057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation da... | [
"# es_financial\n\nThis model is a fine-tuned version of huranokuma/es2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4057\n- Accuracy: 0.9057",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# es_financial\n\nThis model is a fine-tuned version of huranokuma/es2 on an unknown dataset.\nIt achieves the following re... |
text-classification | null |
# Fake News Recognition
## Overview
This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).
LABEL_0: Fake news
LABEL_1: Real news
## Qucik Tutorial
... | {"license": "apache-2.0", "pipeline_tag": "text-classification"} | ShreySavaliya/FakeNewsDetection | null | [
"text-classification",
"license:apache-2.0",
"region:us"
] | null | 2022-08-14T14:04:32+00:00 | [] | [] | TAGS
#text-classification #license-apache-2.0 #region-us
|
# Fake News Recognition
## Overview
This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).
LABEL_0: Fake news
LABEL_1: Real news
## Qucik Tutorial
... | [
"# Fake News Recognition",
"## Overview\n\nThis model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).\n\nLABEL_0: Fake news\nLABEL_1: Real news",
"## Quci... | [
"TAGS\n#text-classification #license-apache-2.0 #region-us \n",
"# Fake News Recognition",
"## Overview\n\nThis model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated auto... |
text-classification | transformers |
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [emotion]( dataset(https://huggingface.co/datasets/emotion) dataset for in the dataset in HG. It achieves the following results on the evaluation set:
- ... | {"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... | aambrioso/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T14:19:50+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 for in the dataset in HG. It achieves the following results on the evaluation set:
* Loss: 0.2033
* Accuracy: 0.9275
* F1: 0.9273
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["eoir_privacy"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy-longer", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "eoir_p... | muhtasham/bert-small-finetuned-eoir_privacy-longer10 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:eoir_privacy",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T14:24:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-eoir\_privacy-longer
=============================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the eoir\_privacy dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1738
* Accuracy: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy-longer-finetuned-eoir_privacy-longer20
This model was trained from scratch on ... | {"tags": ["generated_from_trainer"], "datasets": ["eoir_privacy"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy-longer-finetuned-eoir_privacy-longer20", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": ... | muhtasham/bert-small-finetuned-eoir_privacy-longer20 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:eoir_privacy",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T15:09:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-eoir\_privacy-longer-finetuned-eoir\_privacy-longer20
==============================================================================================
This model was trained from scratch on the eoir\_privacy dataset.
It achieves the following results on the evaluation set:
* L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_ra... |
text-classification | transformers | # Fake News Recognition
## Overview
This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).
LABEL_0: Fake news
LABEL_1: Real news
## Qucik Tutorial
### Do... | {"license": "apache-2.0"} | ShreySavaliya/FakeNewsDetection_1 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T15:29:43+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Fake News Recognition
## Overview
This model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).
LABEL_0: Fake news
LABEL_1: Real news
## Qucik Tutorial
### Do... | [
"# Fake News Recognition",
"## Overview\n\nThis model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply entering the text of the news less than 500 words(the excess will be truncated automatically).\n\nLABEL_0: Fake news\nLABEL_1: Real news",
"## Quci... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fake News Recognition",
"## Overview\n\nThis model is trained by over 40,000 news from different medias based on the 'roberta-base'. It can give result by simply enterin... |
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_uncased_L-4_H-512_A-8-finetuned-eoir_privacy-longer-finetuned-eoir_privacy-longer30
This model was trained from scratch on ... | {"tags": ["generated_from_trainer"], "datasets": ["eoir_privacy"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy-longer-finetuned-eoir_privacy-longer30", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": ... | muhtasham/bert-small-finetuned-eoir-privacy-longer30 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:eoir_privacy",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T15:51:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-eoir\_privacy-longer-finetuned-eoir\_privacy-longer30
==============================================================================================
This model was trained from scratch on the eoir\_privacy dataset.
It achieves the following results on the evaluation set:
* L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-eoir_privacy #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* ... |
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. -->
# TGL-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an abstract-summary dataset,
23000 p... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "TGL-3", "results": []}]} | awesometeng/TGL-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T15:52:15+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TGL-3
=====
This model is a fine-tuned version of t5-small on an abstract-summary dataset,
23000 pieces of data for training. The data was acquired by URL.
It achieves the following results on the evaluation set:
* Loss: 2.4435
* Rouge1: 36.4998
* Rouge2: 17.8322
* Rougel: 31.8632
* Rougelsum: 31.8341
Model descr... | [
"### 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 #t5 #text2text-generation #summarization #generated_from_trainer #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
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. -->
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intend... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "tmp_trainer", "results": []}]} | andres-gv/tmp_trainer | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T16:27:09+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparamete... | [
"# tmp_trainer\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameter... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# tmp_trainer\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | trissondon/test_pyramids_RND | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-14T16:30:32+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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. -->
# mu-phobert-v1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following r... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mu-phobert-v1", "results": []}]} | keepitreal/mu-phobert-v1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T17:26:33+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mu-phobert-v1
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 7.6130
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# mu-phobert-v1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 7.6130",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\n... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mu-phobert-v1\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 7.6130",
"## Model descri... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | trissondon/test_pyramids_RND_2 | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-14T17:38:31+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
reinforcement-learning | 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... | slarionne/PPO_Lander_original_data | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-14T18:32:15+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# diffusion
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/d... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "CelebA", "metrics": []} | shalpin87/diffusion_celeba | null | [
"diffusers",
"en",
"dataset:CelebA",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-14T18:34:06+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-CelebA #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# diffusion
## Model description
This diffusion model is trained with the Diffusers library
on the 'CelebA' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used... | [
"# diffusion",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'CelebA' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training data\n\n[T... | [
"TAGS\n#diffusers #en #dataset-CelebA #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# diffusion",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'CelebA' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias... |
sentence-similarity | tfhub |
## Model name: universal-sentence-encoder
## Description adapted from [TFHub](https://tfhub.dev/google/universal-sentence-encoder/4)
# Overview
The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural languag... | {"language": "en", "license": "apache-2.0", "library_name": "tfhub", "tags": ["text", "sentence-similarity", "use", "universal-sentence-encoder", "dan", "tensorflow"]} | Dimitre/universal-sentence-encoder | null | [
"tfhub",
"keras",
"text",
"sentence-similarity",
"use",
"universal-sentence-encoder",
"dan",
"tensorflow",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-14T18:44:46+00:00 | [] | [
"en"
] | TAGS
#tfhub #keras #text #sentence-similarity #use #universal-sentence-encoder #dan #tensorflow #en #license-apache-2.0 #has_space #region-us
|
## Model name: universal-sentence-encoder
## Description adapted from TFHub
# Overview
The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.
The model is trained and optimized for greater-... | [
"## Model name: universal-sentence-encoder",
"## Description adapted from TFHub",
"# Overview\n\nThe Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.\n\nThe model is trained and optim... | [
"TAGS\n#tfhub #keras #text #sentence-similarity #use #universal-sentence-encoder #dan #tensorflow #en #license-apache-2.0 #has_space #region-us \n",
"## Model name: universal-sentence-encoder",
"## Description adapted from TFHub",
"# Overview\n\nThe Universal Sentence Encoder encodes text into high-dimensiona... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp
This model is a fine-tuned version of [google/mt5-small](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp", "results": []}]} | bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T18:56:40+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp
==========================================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2757
* Validation Loss: 0.2210
* Epoch: 7
Mod... | [
"### 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': 5.6e-05, 'decay\\_steps': 7656, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #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* optimizer: {'name': 'Adam... |
text-generation | transformers |
# Zuko DialoGPT Model | {"tags": ["conversational"]} | chulainn/DialoGPT-medium-Zuko | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-14T19:39:57+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Zuko DialoGPT Model | [
"# Zuko DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Zuko DialoGPT Model"
] |
image-classification | tfhub |
## Model name: mobilenet_v3_small_100_224
## Description adapted from [TFHub](https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/classification/5)
# Overview
MobileNet V3 is a family of neural network architectures for efficient on-device image classification and related tasks, originally published by
- A... | {"license": "apache-2.0", "library_name": "tfhub", "tags": ["vision", "image-classification", "mobilenet", "tensorflow"], "datasets": ["imagenet-1k"], "metrics": ["accuracy"]} | Dimitre/mobilenet_v3_small | null | [
"tfhub",
"keras",
"vision",
"image-classification",
"mobilenet",
"tensorflow",
"dataset:imagenet-1k",
"arxiv:1905.02244",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-14T20:37:05+00:00 | [
"1905.02244"
] | [] | TAGS
#tfhub #keras #vision #image-classification #mobilenet #tensorflow #dataset-imagenet-1k #arxiv-1905.02244 #license-apache-2.0 #has_space #region-us
| Model name: mobilenet\_v3\_small\_100\_224
------------------------------------------
Description adapted from TFHub
------------------------------
Overview
========
MobileNet V3 is a family of neural network architectures for efficient on-device image classification and related tasks, originally published by
*... | [
"### Using TF Hub and HF Hub",
"### Using TF Hub fork\n\n\nThe output is a batch of logits vectors. The indices into the logits are the 'num\\_classes' = 1001 classes of the classification from the original training (see above). The mapping from indices to class labels can be found in the file at URL (with class ... | [
"TAGS\n#tfhub #keras #vision #image-classification #mobilenet #tensorflow #dataset-imagenet-1k #arxiv-1905.02244 #license-apache-2.0 #has_space #region-us \n",
"### Using TF Hub and HF Hub",
"### Using TF Hub fork\n\n\nThe output is a batch of logits vectors. The indices into the logits are the 'num\\_classes' ... |
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