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# Dumb Language Model
Just a dumb language model. | {"language": ["en"], "license": "mit", "tags": ["language-models"], "datasets": ["wikitext103"], "metrics": ["perplexity", "accuracy"]} | rkingery/dumb-language-model | null | [
"language-models",
"en",
"dataset:wikitext103",
"license:mit",
"region:us"
] | null | 2022-06-27T17:08:27+00:00 | [] | [
"en"
] | TAGS
#language-models #en #dataset-wikitext103 #license-mit #region-us
|
# Dumb Language Model
Just a dumb language model. | [
"# Dumb Language Model\nJust a dumb language model."
] | [
"TAGS\n#language-models #en #dataset-wikitext103 #license-mit #region-us \n",
"# Dumb Language Model\nJust a dumb language model."
] |
text2text-generation | transformers | Dataset trained on: https://huggingface.co/datasets/Adapting/empathetic_dialogues_with_special_tokens
Commit hash of model versions
1. blenderbot-400M-distill - 10 epochs fine-tuning: **b86f62986872b4c1a9921acdb8cd226761d736cf**
2. blenderbot-400M-distill - 20 epochs fine-tuning: **e803a10542ea7e4f116e89aca0f7250fb71a... | {} | Adapting/dialogue_agent_nlplab2022 | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T17:51:31+00:00 | [] | [] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Dataset trained on: URL
Commit hash of model versions
1. blenderbot-400M-distill - 10 epochs fine-tuning: b86f62986872b4c1a9921acdb8cd226761d736cf
2. blenderbot-400M-distill - 20 epochs fine-tuning: e803a10542ea7e4f116e89aca0f7250fb71a8a04
3. blenderbot-400M-distill - 30 epochs fine-tuning: 4e9e1331124134dc879adcbad6c... | [] | [
"TAGS\n#transformers #pytorch #blenderbot #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \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. -->
# gpt2-discordgpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-discordgpt2", "results": []}]} | hidude562/gpt2-discordgpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T18:18:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-discordgpt2
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.3032
- eval_runtime: 59.2004
- eval_samples_per_second: 274.542
- eval_steps_per_second: 34.324
- epoch: 0.26
- step: 25500
## Model description
More informati... | [
"# gpt2-discordgpt2\n\nThis model is a fine-tuned version of gpt2 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 5.3032\n- eval_runtime: 59.2004\n- eval_samples_per_second: 274.542\n- eval_steps_per_second: 34.324\n- epoch: 0.26\n- step: 25500",
"## Model description\... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-discordgpt2\n\nThis model is a fine-tuned version of gpt2 on the None dataset.\nIt achieves the following results on... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-mnli-finetuned-header-classifier
This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-large-mnli-finetuned-header-classifier", "results": []}]} | alk/roberta-large-mnli-finetuned-header-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T18:21:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-large-mnli-finetuned-header-classifier
This model is a fine-tuned version of roberta-large-mnli on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# roberta-large-mnli-finetuned-header-classifier\n\nThis model is a fine-tuned version of roberta-large-mnli on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-large-mnli-finetuned-header-classifier\n\nThis model is a fine-tuned version of roberta-large-mnli on the None dataset."... |
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. -->
# charles-dickens
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "charles-dickens", "results": []}]} | Dizzykong/charles-dickens | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T18:27:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# charles-dickens
This model is a fine-tuned version of gpt2-medium 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 follo... | [
"# charles-dickens\n\nThis model is a fine-tuned version of gpt2-medium 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",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# charles-dickens\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore informat... |
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. -->
# NilavoBoral/nilavo-bert-finetuned
This model was trained from scratch on an unknown dataset.
It achieves the following results on the ... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "NilavoBoral/nilavo-bert-finetuned", "results": []}]} | NilavoBoral/nilavo-bert-finetuned | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T18:28:12+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| NilavoBoral/nilavo-bert-finetuned
=================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0018
* Validation Loss: 0.0755
* Epoch: 4
Model description
-----------------
More information needed
Intended... | [
"### 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': 8780, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #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\\_nam... |
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... | MrNoOne/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-27T19:05:02+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1046236000
- CO2 Emissions (in grams): 4581.794954519826
## Validation Metrics
- Loss: 1.4225560426712036
- Rouge1: 42.5931
- Rouge2: 20.0106
- RougeL: 29.681
- RougeLsum: 39.8097
- Gen Len: 84.9844
## Usage
You can use cURL to access this ... | {"language": "unk", "tags": "autotrain", "datasets": ["nizamudma/autotrain-data-text1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4581.794954519826} | nizamudma/bart_cnn_auto | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"unk",
"dataset:nizamudma/autotrain-data-text1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T20:36:09+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain #unk #dataset-nizamudma/autotrain-data-text1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1046236000
- CO2 Emissions (in grams): 4581.794954519826
## Validation Metrics
- Loss: 1.4225560426712036
- Rouge1: 42.5931
- Rouge2: 20.0106
- RougeL: 29.681
- RougeLsum: 39.8097
- Gen Len: 84.9844
## Usage
You can use cURL to access this ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1046236000\n- CO2 Emissions (in grams): 4581.794954519826",
"## Validation Metrics\n\n- Loss: 1.4225560426712036\n- Rouge1: 42.5931\n- Rouge2: 20.0106\n- RougeL: 29.681\n- RougeLsum: 39.8097\n- Gen Len: 84.9844",
"## Usage\n\nYou can... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain #unk #dataset-nizamudma/autotrain-data-text1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1046236000\n- CO2 Emissions (in grams): 458... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jonaskoenig/destillbert-uncased-future_statements
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jonaskoenig/destillbert-uncased-future_statements", "results": []}]} | jonaskoenig/destillbert-uncased-future_statements | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T20:54:32+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jonaskoenig/destillbert-uncased-future\_statements
==================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0092
* Train Sparse Categorical Accuracy: 0.9975
* Valid... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | Abdelmageed95/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T21:27:02+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
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6421
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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/1525581631020576771/qgSl... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/reallifemera/1656476064337/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/reallifemera | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T21:32:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mera Brown
@reallifemera
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-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229
## Validation Metrics
- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.7503... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-glue1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.869994913020229} | deepesh0x/autotrain-glue1-1046836019 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:deepesh0x/autotrain-data-glue1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T22:57:47+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-glue1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229
## Validation Metrics
- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.7503... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1046836019\n- CO2 Emissions (in grams): 3.869994913020229",
"## Validation Metrics\n\n- Loss: 0.626447856426239\n- Accuracy: 0.6606574761399788\n- Precision: 0.6925845932325414\n- Recall: 0.8187234042553192\n- AUC: 0.6564048238... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-glue1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1046836019\n- CO2 Emissions (in gram... |
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | Aalaa/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T00:45:26+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
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6421
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
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. -->
# Negation_Scope_Detection_SFU_Spanish_NLP-CIC-WFU_DisTEMIST_fine_tuned
This model is a fine-tuned version of [ajtamayoh/NER_EHR_S... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Negation_Scope_Detection_SFU_Spanish_NLP-CIC-WFU_DisTEMIST_fine_tuned", "results": []}]} | ajtamayoh/Negation_Scope_Detection_SFU_Spanish_NLP-CIC-WFU_DisTEMIST_fine_tuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T00:50:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Negation\_Scope\_Detection\_SFU\_Spanish\_NLP-CIC-WFU\_DisTEMIST\_fine\_tuned
=============================================================================
This model is a fine-tuned version of ajtamayoh/NER\_EHR\_Spanish\_model\_Mulitlingual\_BERT on the None dataset.
It achieves the following results on the evaluat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | jmwolf27/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 | 2022-06-28T01:00:06+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: 0.3167
- Accuracy: 0.8767
- F1: 0.8779
## 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: 0.3167\n- Accuracy: 0.8767\n- F1: 0.8779",
"## 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... |
fill-mask | transformers | For the detail, see [github:mmdjiji/bert-chinese-idioms](https://github.com/mmdjiji/bert-chinese-idioms). | {"license": "gpl-3.0"} | mmdjiji/bert-chinese-idioms | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T01:02:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| For the detail, see github:mmdjiji/bert-chinese-idioms. | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-CTC Large (de)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Languag... | {"language": ["de"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["VoxPopuli-(DE)", "Multilingual-LibriSpeech", "mozilla-foundation/common_voice_7_0"], "widget... | nvidia/stt_de_conformer_ctc_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"de",
"arxiv:2005.08100",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-06-28T01:36:01+00:00 | [
"2005.08100"
] | [
"de"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #de #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Conformer-CTC Large (de)
===============================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lowercase German alphabet inclu... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 kHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #de #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-125m-finetuned-wikitext2
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m)... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-125m-finetuned-wikitext2", "results": []}]} | Aalaa/opt-125m-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T01:41:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| opt-125m-finetuned-wikitext2
============================
This model is a fine-tuned version of facebook/opt-125m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3409
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-Transducer Large (de)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture... | {"language": ["de"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["VoxPopuli-(DE)", "multilingual_librispeech", "mozilla-foundation/common_voice_7_0"], "widget": [{"ex... | nvidia/stt_de_conformer_transducer_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"de",
"arxiv:2005.08100",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-06-28T01:45:53+00:00 | [
"2005.08100"
] | [
"de"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #de #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us
| NVIDIA Conformer-Transducer Large (de)
======================================
img {
display: inline;
}
| 
| 
| 
This model transcribes speech in lower case German alphabet along with spaces.
It is a "large" versions... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model provides transcribed speech... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #de #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample\n\n\nThen simpl... |
text-generation | transformers |
# Koishi Komeiji DialoGPT Model | {"tags": ["conversational"]} | Hartmann/DialoGPT-small-koishikomeiji | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T02:36:59+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Koishi Komeiji DialoGPT Model | [
"# Koishi Komeiji DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Koishi Komeiji DialoGPT Model"
] |
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... | jcmc/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T02:40:33+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | 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... | mastak128/unit1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T03:19:30+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Nabby/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T03:21:21+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **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... | vebie91/dqn-SpaceInvadersNoFrameskip-v4-1.2 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T03:33:19+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... |
automatic-speech-recognition | nemo |
# NVIDIA Streaming Citrinet 1024 (zh)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Lang... | {"language": ["zh"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Citrinet", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["aishell_2"], "model-index": [{"name": "stt_zh_citrinet_1024_gamma_0_25", "results": [{"task": {"type": "auto... | nvidia/stt_zh_citrinet_1024_gamma_0_25 | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Citrinet",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"zh",
"dataset:aishell_2",
"arxiv:2104.01721",
"license:cc-by-4.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-06-28T03:42:09+00:00 | [
"2104.01721"
] | [
"zh"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #pytorch #NeMo #hf-asr-leaderboard #Riva #zh #dataset-aishell_2 #arxiv-2104.01721 #license-cc-by-4.0 #model-index #has_space #region-us
| NVIDIA Streaming Citrinet 1024 (zh)
===================================
img {
display: inline;
}
| 
| 
| 
|  |
This model utilizes a character encoding scheme, and tra... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a sample of spoken Mandarin Chinese.\n\n\nThen simply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16000 kHz Mono-channel Audio (wav files) as input.",
"### Output\n\n\nThis model... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Citrinet #pytorch #NeMo #hf-asr-leaderboard #Riva #zh #dataset-aishell_2 #arxiv-2104.01721 #license-cc-by-4.0 #model-index #has_space #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nFirst, let's get a ... |
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="AdiKompella/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | AdiKompella/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-28T04:46:20+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AdiKompella/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.46 +/... | AdiKompella/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-28T04:49:24+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"
] |
summarization | transformers |
# t5-small for headline generation
This model is a [t5-small](https://huggingface.co/t5-small) fine-tuned for headline generation using
the [JulesBelveze/tldr_news](https://huggingface.co/datasets/JulesBelveze/tldr_news) dataset.
## Using this model
```python
import re
from transformers import AutoTokenizer, T5ForCo... | {"language": ["en"], "license": "mit", "tags": ["summarization", "headline-generation", "text-generation"], "datasets": ["JulesBelveze/tldr_news"], "metrics": ["rouge1", "rouge2", "rougeL", "rougeLsum"]} | JulesBelveze/t5-small-headline-generator | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"headline-generation",
"text-generation",
"en",
"dataset:JulesBelveze/tldr_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us... | null | 2022-06-28T04:51:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #headline-generation #text-generation #en #dataset-JulesBelveze/tldr_news #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-small for headline generation
================================
This model is a t5-small fine-tuned for headline generation using
the JulesBelveze/tldr\_news dataset.
Using this model
----------------
Evaluation
----------
| [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #summarization #headline-generation #text-generation #en #dataset-JulesBelveze/tldr_news #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-1
This model is a fine-tuned version of [gary109/ai-light-dance_singi... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-1", "results": []}]} | gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v4-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T04:51:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53-5gram-v4-1
===============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing\_ft\_wav2vec2-large-xlsr-53-5gram-v4 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING2 dataset.
It achieves the following... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-CTC Large (fr)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Languag... | {"language": "fr", "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["multilingual_librispeech", "mozilla-foundation/common_voice_7_0", "VoxPopuli"], "model-index":... | nvidia/stt_fr_conformer_ctc_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"fr",
"dataset:multilingual_librispeech",
"dataset:mozilla-foundation/common_voice_7_0",
"dataset:VoxPopuli",
"arxiv:2005.08100",
"license:cc... | null | 2022-06-28T05:32:05+00:00 | [
"2005.08100"
] | [
"fr"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #fr #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_7_0 #dataset-VoxPopuli #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us
|
# NVIDIA Conformer-CTC Large (fr)
<style>
img {
display: inline;
}
</style>
| 
| 
| 
|  |
This model was trained on a composite dataset comprising of over 1500 hours of... | [
"# NVIDIA Conformer-CTC Large (fr)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| \n| \n| \n|  |\n\n\nThis model was trained on a composite dataset comprising of ... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #fr #dataset-multilingual_librispeech #dataset-mozilla-foundation/common_voice_7_0 #dataset-VoxPopuli #arxiv-2005.08100 #license-cc-by-4.0 #model-index #region-us \n",
"# NVIDIA Conforme... |
text-classification | transformers |
# Note
`Aspect term sentiment analysis`
BERT LSTM based baseline, based on https://github.com/avinashsai/BERT-Aspect *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.
Our Github repo: https://github.com/tezignlab/BERT-LSTM-based-ABSA
Code for the paper "Utilizing BE... | {"language": "en", "tags": ["aspect-term-sentiment-analysis", "pytorch", "ATSA"], "datasets": ["semeval2014"], "widget": [{"text": "[CLS] The appearance is very nice, but the battery life is poor. [SEP] appearance [SEP] "}]} | tezign/BERT-LSTM-based-ABSA | null | [
"transformers",
"pytorch",
"BertABSAForSequenceClassification",
"text-classification",
"aspect-term-sentiment-analysis",
"ATSA",
"custom_code",
"en",
"dataset:semeval2014",
"arxiv:2002.04815",
"autotrain_compatible",
"region:us"
] | null | 2022-06-28T06:02:53+00:00 | [
"2002.04815"
] | [
"en"
] | TAGS
#transformers #pytorch #BertABSAForSequenceClassification #text-classification #aspect-term-sentiment-analysis #ATSA #custom_code #en #dataset-semeval2014 #arxiv-2002.04815 #autotrain_compatible #region-us
|
# Note
'Aspect term sentiment analysis'
BERT LSTM based baseline, based on URL *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.
Our Github repo: URL
Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Infe... | [
"# Note\n\n'Aspect term sentiment analysis'\n\nBERT LSTM based baseline, based on URL *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.\n\nOur Github repo: URL\n\nCode for the paper \"Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural ... | [
"TAGS\n#transformers #pytorch #BertABSAForSequenceClassification #text-classification #aspect-term-sentiment-analysis #ATSA #custom_code #en #dataset-semeval2014 #arxiv-2002.04815 #autotrain_compatible #region-us \n",
"# Note\n\n'Aspect term sentiment analysis'\n\nBERT LSTM based baseline, based on URL *BERT LSTM... |
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... | dwing/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 | 2022-06-28T06:15:44+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: 0.1616
* Accuracy: 0.9335
* F1: 0.9337
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | null | SpongeBob cow | {} | Umud/Asgar | null | [
"region:us"
] | null | 2022-06-28T06:27:24+00:00 | [] | [] | TAGS
#region-us
| SpongeBob cow | [] | [
"TAGS\n#region-us \n"
] |
null | null | This is just me playing around with Hugging Face :-) | {} | rtorrero/my-first-model | null | [
"region:us"
] | null | 2022-06-28T06:41:49+00:00 | [] | [] | TAGS
#region-us
| This is just me playing around with Hugging Face :-) | [] | [
"TAGS\n#region-us \n"
] |
null | null | sex | {} | Shadowiscoolsoomg/banana | null | [
"region:us"
] | null | 2022-06-28T06:50:26+00:00 | [] | [] | TAGS
#region-us
| sex | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers | Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP | {"license": "afl-3.0"} | sumitrsch/muril_base_multiconer22_hi | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T06:57:21+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. URL | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | adapter-transformers |
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... | {"language": "en", "license": "apache-2.0", "library_name": "adapter-transformers", "tags": ["exbert"], "datasets": ["gsdf/EasyNega"], "metrics": ["accuracy"], "text": "aaaaaaa", "pipeline_tag": "image-to-i"} | AIKey/test | null | [
"adapter-transformers",
"exbert",
"image-to-i",
"en",
"dataset:gsdf/EasyNega",
"license:apache-2.0",
"region:us"
] | null | 2022-06-28T08:31:52+00:00 | [] | [
"en"
] | TAGS
#adapter-transformers #exbert #image-to-i #en #dataset-gsdf/EasyNega #license-apache-2.0 #region-us
| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#adapter-transformers #exbert #image-to-i #en #dataset-gsdf/EasyNega #license-apache-2.0 #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this... |
fill-mask | transformers |
Model BERTuit as presented in the [BERTuit: Understanding Spanish language in Twitter through a native transformer](https://arxiv.org/abs/2204.03465) article.
Before tokenization replace user tags and urls with "\<usr\>" and "\<url\>" respectively.
Tokenize text with base class RoBERTaTokenizer. | {"language": "es", "license": "apache-2.0", "tags": ["online social networks", "twitter", "spanish"], "pipeline_tag": "fill-mask", "widget": [{"text": "Las <mask> causan hipoxia.", "example_title": "Mask filling"}]} | AIDA-UPM/BERTuit-base | null | [
"transformers",
"tf",
"roberta",
"online social networks",
"twitter",
"spanish",
"fill-mask",
"es",
"arxiv:2204.03465",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T08:55:34+00:00 | [
"2204.03465"
] | [
"es"
] | TAGS
#transformers #tf #roberta #online social networks #twitter #spanish #fill-mask #es #arxiv-2204.03465 #license-apache-2.0 #endpoints_compatible #region-us
|
Model BERTuit as presented in the BERTuit: Understanding Spanish language in Twitter through a native transformer article.
Before tokenization replace user tags and urls with "\<usr\>" and "\<url\>" respectively.
Tokenize text with base class RoBERTaTokenizer. | [] | [
"TAGS\n#transformers #tf #roberta #online social networks #twitter #spanish #fill-mask #es #arxiv-2204.03465 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test_Model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Mo... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "test_Model", "results": []}]} | muhammedshihebi/test_Model | null | [
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T09:31:50+00:00 | [] | [] | TAGS
#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #endpoints_compatible #region-us
|
# test_Model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# test_Model\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #tf #xlm-roberta #question-answering #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# test_Model\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed"... |
feature-extraction | 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. -->
# led-large-16384-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/led-large-16384-finetuned-big_patent](https://... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "led-large-16384-finetuned-big_patent", "results": []}]} | robingeibel/led-large-16384-finetuned-big_patent | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"led",
"feature-extraction",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T09:32:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
|
# led-large-16384-finetuned-big_patent
This model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# led-large-16384-finetuned-big_patent\n\nThis model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informat... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"# led-large-16384-finetuned-big_patent\n\nThis model is a fine-tuned version of robingeibel/led-large-16384-finetuned-big_patent on an unknown dataset.\... |
image-segmentation | transformers |
# MobileNetV2 with DeepLabV3+
MobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["pascal-voc"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg", "example_title": "Cat"}]} | Matthijs/deeplabv3_mobilenet_v2_1.0_513 | null | [
"transformers",
"pytorch",
"coreml",
"mobilenet_v2",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:1801.04381",
"arxiv:1802.02611",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T10:16:06+00:00 | [
"1801.04381",
"1802.02611"
] | [] | TAGS
#transformers #pytorch #coreml #mobilenet_v2 #vision #image-segmentation #dataset-pascal-voc #arxiv-1801.04381 #arxiv-1802.02611 #license-other #endpoints_compatible #region-us
|
# MobileNetV2 with DeepLabV3+
MobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository.
Disclaimer: The tea... | [
"# MobileNetV2 with DeepLabV3+\n\nMobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository.\n\nDisclaimer:... | [
"TAGS\n#transformers #pytorch #coreml #mobilenet_v2 #vision #image-segmentation #dataset-pascal-voc #arxiv-1801.04381 #arxiv-1802.02611 #license-other #endpoints_compatible #region-us \n",
"# MobileNetV2 with DeepLabV3+\n\nMobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in Mo... |
token-classification | allennlp |
# BERTu for language-specific Part-of-Speech Tagging (XPOS)
This is a fine-tuned version of [BERTu](https://huggingface.co/MLRS/BERTu) on Part-of-Speech Tagging using the [language-specific tagset](https://mlrs.research.um.edu.mt/resources/malti03/tagset30.html).
To make use of this model, customised modules are need... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "tags": ["part-of-speech", "token-classification", "allennlp"], "datasets": ["mlrs_pos"]} | MLRS/BERTu-xpos | null | [
"allennlp",
"tensorboard",
"part-of-speech",
"token-classification",
"mt",
"dataset:mlrs_pos",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-28T10:58:53+00:00 | [] | [
"mt"
] | TAGS
#allennlp #tensorboard #part-of-speech #token-classification #mt #dataset-mlrs_pos #license-cc-by-nc-sa-4.0 #region-us
|
# BERTu for language-specific Part-of-Speech Tagging (XPOS)
This is a fine-tuned version of BERTu on Part-of-Speech Tagging using the language-specific tagset.
To make use of this model, customised modules are needed; refer to the codebase for more details.
## License
Refer to the base model licensing information.
... | [
"# BERTu for language-specific Part-of-Speech Tagging (XPOS)\n\nThis is a fine-tuned version of BERTu on Part-of-Speech Tagging using the language-specific tagset.\nTo make use of this model, customised modules are needed; refer to the codebase for more details.",
"## License\n\nRefer to the base model licensing ... | [
"TAGS\n#allennlp #tensorboard #part-of-speech #token-classification #mt #dataset-mlrs_pos #license-cc-by-nc-sa-4.0 #region-us \n",
"# BERTu for language-specific Part-of-Speech Tagging (XPOS)\n\nThis is a fine-tuned version of BERTu on Part-of-Speech Tagging using the language-specific tagset.\nTo make use of thi... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **seals/CartPole-v0**
This is a trained model of a **PPO** agent playing **seals/CartPole-v0**
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 framework for Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["seals/CartPole-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/CartPole-v0", "type": "... | ernestumorga/ppo-seals-CartPole-v0 | null | [
"stable-baselines3",
"seals/CartPole-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T11:06:37+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/CartPole-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing seals/CartPole-v0
This is a trained model of a PPO agent playing seals/CartPole-v0
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 included.
## Usage... | [
"# PPO Agent playing seals/CartPole-v0\nThis is a trained model of a PPO agent playing seals/CartPole-v0\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-trained agents included... | [
"TAGS\n#stable-baselines3 #seals/CartPole-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing seals/CartPole-v0\nThis is a trained model of a PPO agent playing seals/CartPole-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training ... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | alleniver/my_test_cat | null | [
"fastai",
"region:us"
] | null | 2022-06-28T11:12:21+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
text-classification | transformers |
This is a [ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large) model trained on the [RuCoLa](https://rucola-benchmark.com/) dataset. It can be used to classify Russian sentences into fluent or non-fluent ones, where fluency is understood as linguistic acceptability.
Training notebook: `task_oriented... | {"language": ["ru"], "tags": ["fluency"]} | s-nlp/ruRoberta-large-RuCoLa-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"fluency",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T11:46:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #fluency #ru #autotrain_compatible #endpoints_compatible #region-us
|
This is a ruRoberta-large model trained on the RuCoLa dataset. It can be used to classify Russian sentences into fluent or non-fluent ones, where fluency is understood as linguistic acceptability.
Training notebook: 'task_oriented_TST/fluency/rucola_classifier_v1.ipynb' (in a private repo).
Training parameters:
*... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #fluency #ru #autotrain_compatible #endpoints_compatible #region-us \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. -->
# bigbird-large-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/bigbird-large-finetuned-big_patent](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["big_patent"], "model-index": [{"name": "bigbird-large-finetuned-big_patent", "results": []}]} | robingeibel/bigbird-large-finetuned-big_patent | null | [
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"fill-mask",
"generated_from_trainer",
"dataset:big_patent",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T11:53:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #big_bird #fill-mask #generated_from_trainer #dataset-big_patent #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bigbird-large-finetuned-big\_patent
===================================
This model is a fine-tuned version of robingeibel/bigbird-large-finetuned-big\_patent on the big\_patent dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0460
Model description
-----------------
More information n... | [
"### 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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #big_bird #fill-mask #generated_from_trainer #dataset-big_patent #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* t... |
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. -->
# BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc", "results": []}]} | BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T12:07:54+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc
=========================================================
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: 0.0209
* Validation Loss: 0.0320
* Epoch: 2
Model ... | [
"### 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': 705, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"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\\_... |
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/1501241215433510919/4Gct... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/gregorian000-levelsio | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T12:11:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
David & @levelsio
@gregorian000-levelsio
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.
Trai... | [] | [
"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... | choonlee/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T12:11:51+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... |
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/959389610978742273/jfOMG... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/g__j | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T12:36:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Greg Jackson
@g\_\_j
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 |
<!-- 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": []}]} | moonzi/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 | 2022-06-28T12:37:36+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: 2.4702
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... |
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. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v6
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v6", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v6 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T12:47:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v6
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v5 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* ... |
text2text-generation | transformers |
# Model Card of `lmqg/mbart-large-cc25-koquad-qg`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "ko", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_koquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "1990\ub144 \uc601\ud654 \u300a <hl> \ub0a8\ubd80\uad70 <hl> \u300b\uc5d0\uc11c \u... | research-backup/mbart-large-cc25-koquad-qg | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question generation",
"ko",
"dataset:lmqg/qg_koquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T12:47:13+00:00 | [
"2210.03992"
] | [
"ko"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/mbart-large-cc25-koquad-qg'
===============================================
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg\_koquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/mbart-large-cc25
* Language... | [
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: ko\n* Training data: lmqg/qg\\_koquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question generation #ko #dataset-lmqg/qg_koquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: ko\n* Training data:... |
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... | xliu128/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 | 2022-06-28T12:51:01+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: 0.2168
* Accuracy: 0.925
* F1: 0.9247
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 | 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... | {"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... | uvd174/baseline-ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T12:53:24+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-to-image | transformers |
# DALL·E Mega Model Card
This model card focuses on the DALL·E Mega model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incorporates “[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-... | {"language": "en", "license": "apache-2.0", "tags": ["text-to-image"], "inference": false, "co2_eq_emissions": {"emissions": 450300, "source": "MLCo2 Machine Learning Impact calculator", "geographical_location": "East USA", "hardware_used": "TTPU v3-256"}, "task": {"name": "Text to Image", "type": "text-to-image"}, "mo... | dalle-mini/dalle-mega | null | [
"transformers",
"jax",
"dallebart",
"text-to-image",
"en",
"arxiv:1910.09700",
"license:apache-2.0",
"co2_eq_emissions",
"has_space",
"region:us"
] | null | 2022-06-28T13:07:04+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #jax #dallebart #text-to-image #en #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us
|
# DALL·E Mega Model Card
This model card focuses on the DALL·E Mega model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini” and “DALL·E Mega” models. The DALL·E Mega model is the largest version of DALLE Mini. For more information spe... | [
"# DALL·E Mega Model Card\nThis model card focuses on the DALL·E Mega model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini” and “DALL·E Mega” models. The DALL·E Mega model is the largest version of DALLE Mini. For more informatio... | [
"TAGS\n#transformers #jax #dallebart #text-to-image #en #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us \n",
"# DALL·E Mega Model Card\nThis model card focuses on the DALL·E Mega model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-min... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["tr-TR"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["common_voice, common_voice_6_1_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-tr", "results": []}]} | russellc/wav2vec2-large-xls-r-300m-tr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T13:33:00+00:00 | [] | [
"tr-TR"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-tr
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2841
* Wer: 0.2904
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 14\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* trai... |
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. -->
# opus-mt-en-de-finetuned-en-to-de
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-de-finetuned-en-to-de", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | wandgibaut/opus-mt-en-de-finetuned-en-to-de | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T13:41:24+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-de-finetuned-en-to-de
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4083
* Bleu: 29.4312
* Gen Len: 24.746
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: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": ["en"], "tags": ["summarization", "bart", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-bart-base-16384 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"long context",
"fill-mask",
"custom_code",
"en",
"arxiv:2210.15497",
"arxiv:1910.13461",
"autotrain_compatible",
"region:us"
] | null | 2022-06-28T13:44:38+00:00 | [
"2210.15497",
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1910.13461 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
This model is adapted from BART-base for encoder-decoder t... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n\nThis model is adapted from BART-base ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #long context #fill-mask #custom_code #en #arxiv-2210.15497 #arxiv-1910.13461 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\... |
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. -->
# ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v3
This model is a fine-tuned version of [gary109/ai-light-dance_singing... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v3", "results": []}]} | gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53-5gram-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T13:58:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53-5gram-v3
=============================================================
This model is a fine-tuned version of gary109/ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53-v2 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING2 dataset.
It achieves the following results ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size... |
text2text-generation | transformers |
# Spanish Bert2Bert fine-tuned on Quora question pairs dataset
Fine-tuning of a [question generator model](https://huggingface.co/mrm8488/bert2bert-spanish-question-generation) into a paraphraser model using a poor-man's translation of the Quora question pairs dataset. It basically rephrases questions into similar qu... | {"license": "apache-2.0"} | pserna/bert2bert-spanish-paraphraser | null | [
"transformers",
"pytorch",
"tf",
"encoder-decoder",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T14:03:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #encoder-decoder #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Spanish Bert2Bert fine-tuned on Quora question pairs dataset
Fine-tuning of a question generator model into a paraphraser model using a poor-man's translation of the Quora question pairs dataset. It basically rephrases questions into similar questions. Non interrogative sentences are not handled very well.
- Origi... | [
"# Spanish Bert2Bert fine-tuned on Quora question pairs dataset\n\nFine-tuning of a question generator model into a paraphraser model using a poor-man's translation of the Quora question pairs dataset. It basically rephrases questions into similar questions. Non interrogative sentences are not handled very well.\n\... | [
"TAGS\n#transformers #pytorch #tf #encoder-decoder #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Spanish Bert2Bert fine-tuned on Quora question pairs dataset\n\nFine-tuning of a question generator model into a paraphraser model using a poor-man's translat... |
token-classification | allennlp |
# BERTu for language-universal Part-of-Speech Tagging (UPOS)
This is a fine-tuned version of [BERTu](https://huggingface.co/MLRS/BERTu) on Part-of-Speech Tagging using the [language-universal tagset](https://universaldependencies.org/u/pos/index.html).
To make use of this model, customised modules are needed; refer t... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "tags": ["part-of-speech", "token-classification", "allennlp"], "datasets": ["mlrs_pos"]} | MLRS/BERTu-upos | null | [
"allennlp",
"tensorboard",
"part-of-speech",
"token-classification",
"mt",
"dataset:mlrs_pos",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-28T14:13:00+00:00 | [] | [
"mt"
] | TAGS
#allennlp #tensorboard #part-of-speech #token-classification #mt #dataset-mlrs_pos #license-cc-by-nc-sa-4.0 #region-us
|
# BERTu for language-universal Part-of-Speech Tagging (UPOS)
This is a fine-tuned version of BERTu on Part-of-Speech Tagging using the language-universal tagset.
To make use of this model, customised modules are needed; refer to the codebase for more details.
## License
Refer to the base model licensing information... | [
"# BERTu for language-universal Part-of-Speech Tagging (UPOS)\n\nThis is a fine-tuned version of BERTu on Part-of-Speech Tagging using the language-universal tagset.\nTo make use of this model, customised modules are needed; refer to the codebase for more details.",
"## License\n\nRefer to the base model licensin... | [
"TAGS\n#allennlp #tensorboard #part-of-speech #token-classification #mt #dataset-mlrs_pos #license-cc-by-nc-sa-4.0 #region-us \n",
"# BERTu for language-universal Part-of-Speech Tagging (UPOS)\n\nThis is a fine-tuned version of BERTu on Part-of-Speech Tagging using the language-universal tagset.\nTo make use of t... |
token-classification | allennlp |
# BERTu for Dependency Parsing
This is a fine-tuned version of [BERTu](https://huggingface.co/MLRS/BERTu) on Dependency Parsing.
To make use of this model, customised modules are needed; refer to the [codebase](https://github.com/MLRS/BERTu/tree/main/evaluate) for more details.
## License
Refer to the [base model l... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "tags": ["dependency-parsing", "token-classification", "allennlp"], "datasets": ["universal_dependencies"]} | MLRS/BERTu-ud | null | [
"allennlp",
"tensorboard",
"dependency-parsing",
"token-classification",
"mt",
"dataset:universal_dependencies",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-28T14:32:14+00:00 | [] | [
"mt"
] | TAGS
#allennlp #tensorboard #dependency-parsing #token-classification #mt #dataset-universal_dependencies #license-cc-by-nc-sa-4.0 #region-us
|
# BERTu for Dependency Parsing
This is a fine-tuned version of BERTu on Dependency Parsing.
To make use of this model, customised modules are needed; refer to the codebase for more details.
## License
Refer to the base model licensing information.
Refer to the base model citation information. | [
"# BERTu for Dependency Parsing\n\nThis is a fine-tuned version of BERTu on Dependency Parsing.\nTo make use of this model, customised modules are needed; refer to the codebase for more details.",
"## License\n\nRefer to the base model licensing information.\n\nRefer to the base model citation information."
] | [
"TAGS\n#allennlp #tensorboard #dependency-parsing #token-classification #mt #dataset-universal_dependencies #license-cc-by-nc-sa-4.0 #region-us \n",
"# BERTu for Dependency Parsing\n\nThis is a fine-tuned version of BERTu on Dependency Parsing.\nTo make use of this model, customised modules are needed; refer to t... |
token-classification | allennlp |
# BERTu fine-tuned for Named-Entity Recognition
This is a fine-tuned version of [BERTu](https://huggingface.co/MLRS/BERTu) on Named-Entity Recognition.
To make use of this model, customised modules are needed; refer to the [codebase](https://github.com/MLRS/BERTu/tree/main/evaluate) for more details.
## License
Ref... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "tags": ["named-entity-recognition", "token-classification", "allennlp"], "datasets": ["wikiann"]} | MLRS/BERTu-ner | null | [
"allennlp",
"tensorboard",
"named-entity-recognition",
"token-classification",
"mt",
"dataset:wikiann",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-28T14:38:23+00:00 | [] | [
"mt"
] | TAGS
#allennlp #tensorboard #named-entity-recognition #token-classification #mt #dataset-wikiann #license-cc-by-nc-sa-4.0 #region-us
|
# BERTu fine-tuned for Named-Entity Recognition
This is a fine-tuned version of BERTu on Named-Entity Recognition.
To make use of this model, customised modules are needed; refer to the codebase for more details.
## License
Refer to the base model licensing information.
Refer to the base model citation information... | [
"# BERTu fine-tuned for Named-Entity Recognition\n\nThis is a fine-tuned version of BERTu on Named-Entity Recognition.\nTo make use of this model, customised modules are needed; refer to the codebase for more details.",
"## License\n\nRefer to the base model licensing information.\n\nRefer to the base model citat... | [
"TAGS\n#allennlp #tensorboard #named-entity-recognition #token-classification #mt #dataset-wikiann #license-cc-by-nc-sa-4.0 #region-us \n",
"# BERTu fine-tuned for Named-Entity Recognition\n\nThis is a fine-tuned version of BERTu on Named-Entity Recognition.\nTo make use of this model, customised modules are need... |
text-classification | allennlp |
# BERTu for Sentiment Classification
This is a fine-tuned version of [BERTu](https://huggingface.co/MLRS/BERTu) on Sentiment Classification using the [Maltese Sentiment Analysis data](https://github.com/jerbarnes/typology_of_crosslingual/tree/master/data/sentiment/mt).
To make use of this model, customised modules ar... | {"language": ["mt"], "license": "cc-by-nc-sa-4.0", "tags": ["sentiment-analysis", "text-classification", "allennlp"], "datasets": ["mt-sentiment-analysis"]} | MLRS/BERTu-sentiment | null | [
"allennlp",
"tensorboard",
"sentiment-analysis",
"text-classification",
"mt",
"dataset:mt-sentiment-analysis",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-28T14:44:42+00:00 | [] | [
"mt"
] | TAGS
#allennlp #tensorboard #sentiment-analysis #text-classification #mt #dataset-mt-sentiment-analysis #license-cc-by-nc-sa-4.0 #region-us
|
# BERTu for Sentiment Classification
This is a fine-tuned version of BERTu on Sentiment Classification using the Maltese Sentiment Analysis data.
To make use of this model, customised modules are needed; refer to the codebase for more details.
## License
Refer to the base model licensing information.
Refer to the ... | [
"# BERTu for Sentiment Classification\n\nThis is a fine-tuned version of BERTu on Sentiment Classification using the Maltese Sentiment Analysis data.\nTo make use of this model, customised modules are needed; refer to the codebase for more details.",
"## License\n\nRefer to the base model licensing information.\n... | [
"TAGS\n#allennlp #tensorboard #sentiment-analysis #text-classification #mt #dataset-mt-sentiment-analysis #license-cc-by-nc-sa-4.0 #region-us \n",
"# BERTu for Sentiment Classification\n\nThis is a fine-tuned version of BERTu on Sentiment Classification using the Maltese Sentiment Analysis data.\nTo make use of t... |
null | null | can someone teach me how to do this pls help me---
license: isc
---
| {} | Parkerboys211/IDK | null | [
"region:us"
] | null | 2022-06-28T14:44:55+00:00 | [] | [] | TAGS
#region-us
| can someone teach me how to do this pls help me---
license: isc
---
| [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers | This repo contains the fully trained ByT5 that was used to estimate per-character entropies. Using it, you can also recreate the illustration in the paper.
## Citation
If you use this for research, please cite:
```bibtex
@misc{https://doi.org/10.48550/arxiv.2206.12693,
doi = {10.48550/ARXIV.2206.12693},
url = {ht... | {} | fxtentacle/tevr-token-entropy-predictor-de | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2206.12693",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T14:50:45+00:00 | [
"2206.12693"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2206.12693 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This repo contains the fully trained ByT5 that was used to estimate per-character entropies. Using it, you can also recreate the illustration in the paper.
If you use this for research, please cite:
## Generate TEVR Tokenizer from Text corpus
(copy of 'Generate TEVR URL')
Über vier Jahrzehnte gehö... | [
"## Generate TEVR Tokenizer from Text corpus\n(copy of 'Generate TEVR URL')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Über vier Jahrzehnte gehörte er zu den führenden Bildhauern Niederbayerns\n Ü 7.254014\n b 0.17521738\n e 0.00046933602\n r 0.01929327\n 0.0003675739\n v 0.20927554\n i 6.13207\n ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2206.12693 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Generate TEVR Tokenizer from Text corpus\n(copy of 'Generate TEVR URL')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Über vier Jahrzehnte gehörte er zu den füh... |
null | transformers | # distilrubert-tiny-cased-conversational-5k
Conversational DistilRuBERT-tiny-5k \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 3.6M parameters, 5k vocab\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBE... | {"language": ["ru"]} | DeepPavlov/distilrubert-tiny-cased-conversational-5k | null | [
"transformers",
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T15:24:27+00:00 | [
"2205.02340"
] | [
"ru"
] | TAGS
#transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #region-us
| distilrubert-tiny-cased-conversational-5k
=========================================
Conversational DistilRuBERT-tiny-5k (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 3.6M parameters, 5k vocab) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT).
... | [] | [
"TAGS\n#transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #region-us \n"
] |
text-generation | null |
# Azur Lane DialoGPT Model | {"tags": ["conversational"]} | Konbai/DialoGPT-small-akagi | null | [
"conversational",
"region:us"
] | null | 2022-06-28T15:36:57+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Azur Lane DialoGPT Model | [
"# Azur Lane DialoGPT Model"
] | [
"TAGS\n#conversational #region-us \n",
"# Azur Lane DialoGPT Model"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | 745H1N/CarRacing-v0-PPO-optuna | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T15:40:59+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
text-generation | transformers |
# Azur Lane DialoGPT Model | {"tags": ["conversational"]} | Konbai/DialoGPT-small-akagi2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T15:41:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Azur Lane DialoGPT Model | [
"# Azur Lane DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Azur Lane DialoGPT Model"
] |
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... | mariastull/dqn-SpaceInvadersNoFrameSkip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T15:54:53+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... |
null | transformers | # distilrubert-small-cased-conversational
Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://hug... | {"language": ["ru"]} | DeepPavlov/distilrubert-small-cased-conversational | null | [
"transformers",
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-28T16:15:00+00:00 | [
"2205.02340"
] | [
"ru"
] | TAGS
#transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #has_space #region-us
| distilrubert-small-cased-conversational
=======================================
Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT). It can be conside... | [] | [
"TAGS\n#transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #has_space #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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | zunicd/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 | 2022-06-28T16:48:59+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: 0.3349
- Accuracy: 0.8733
- F1: 0.8742
## 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: 0.3349\n- Accuracy: 0.8733\n- F1: 0.8742",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
text-to-speech | nemo | # NVIDIA FastPitch (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Language... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["text-to-speech", "speech", "audio", "Transformer", "pytorch", "NeMo", "Riva"], "datasets": ["ljspeech"]} | nvidia/tts_en_fastpitch | null | [
"nemo",
"text-to-speech",
"speech",
"audio",
"Transformer",
"pytorch",
"NeMo",
"Riva",
"en",
"dataset:ljspeech",
"arxiv:2006.06873",
"arxiv:2108.10447",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-06-28T16:55:51+00:00 | [
"2006.06873",
"2108.10447"
] | [
"en"
] | TAGS
#nemo #text-to-speech #speech #audio #Transformer #pytorch #NeMo #Riva #en #dataset-ljspeech #arxiv-2006.06873 #arxiv-2108.10447 #license-cc-by-4.0 #has_space #region-us
| # NVIDIA FastPitch (en-US)
<style>
img {
display: inline;
}
</style>
| 
| 
| 
|  |
FastPitch [1] is a fully-parallel transformer architecture with prosody control over pi... | [
"# NVIDIA FastPitch (en-US)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| \n| \n| \n|  |\n\nFastPitch [1] is a fully-parallel transformer architecture with proso... | [
"TAGS\n#nemo #text-to-speech #speech #audio #Transformer #pytorch #NeMo #Riva #en #dataset-ljspeech #arxiv-2006.06873 #arxiv-2108.10447 #license-cc-by-4.0 #has_space #region-us \n",
"# NVIDIA FastPitch (en-US)\n\n<style>\nimg {\n display: inline;\n}\n</style>\n\n| \n| ![M... |
question-answering | transformers |
# BERT Base Uncased Finetuned on TriviaQA
The BERT (Base) model is finetuned on the TriviaQA dataset using a modified version of the `run_squad.py` legacy script in Transformers. The script is provided in this repository.
```bash
$ cd ~/projects/transformers/examples/legacy/question-answering
$ mkdir bert_base_uncas... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["trivia_qa"], "metrics": ["f1", "exact_match"]} | mirbostani/bert-base-uncased-finetuned-triviaqa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"dataset:trivia_qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T17:55:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #dataset-trivia_qa #license-apache-2.0 #endpoints_compatible #region-us
|
# BERT Base Uncased Finetuned on TriviaQA
The BERT (Base) model is finetuned on the TriviaQA dataset using a modified version of the 'run_squad.py' legacy script in Transformers. The script is provided in this repository.
Results:
| [
"# BERT Base Uncased Finetuned on TriviaQA\n\nThe BERT (Base) model is finetuned on the TriviaQA dataset using a modified version of the 'run_squad.py' legacy script in Transformers. The script is provided in this repository.\n\n\n\nResults:"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-trivia_qa #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BERT Base Uncased Finetuned on TriviaQA\n\nThe BERT (Base) model is finetuned on the TriviaQA dataset using a modified version of the 'run_squad.py' legacy script in Transfor... |
text-generation | transformers |
# Tony Stark DialoGPT Model | {"tags": ["conversational"]} | JazzyLucas/DialoGPT-small-TonyStark | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T18:25:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tony Stark DialoGPT Model | [
"# Tony Stark DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tony Stark DialoGPT Model"
] |
null | espnet |
## ESPnet2 DIAR model
### `YushiUeda/callhome_adapt_simu`
This model was trained by YushiUeda using callhome recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 0cabe65afd362122e77b04e2e967986a91de0fd8
pip install -e .
cd egs2/callhome/diar1
./run.... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["callhome"]} | YushiUeda/callhome_adapt_simu | null | [
"espnet",
"audio",
"diarization",
"dataset:callhome",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-28T18:32:41+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #diarization #dataset-callhome #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 DIAR model
### 'YushiUeda/callhome_adapt_simu'
This model was trained by YushiUeda using callhome recipe in espnet.
### Demo: How to use in ESPnet2
## DIAR config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 DIAR model",
"### 'YushiUeda/callhome_adapt_simu'\n\nThis model was trained by YushiUeda using callhome recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## DIAR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
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"### Demo: How to use in ESPnet2",
"## DIAR config\n\n<details><su... |
null | espnet |
## ESPnet2 DIAR model
### `YushiUeda/callhome_adapt_real`
This model was trained by YushiUeda using callhome recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 0cabe65afd362122e77b04e2e967986a91de0fd8
pip install -e .
cd egs2/callhome/diar1
./run.... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["callhome"]} | YushiUeda/callhome_adapt_real | null | [
"espnet",
"audio",
"diarization",
"dataset:callhome",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-28T18:34:35+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #diarization #dataset-callhome #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 DIAR model
------------------
### 'YushiUeda/callhome\_adapt\_real'
This model was trained by YushiUeda using callhome recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Jun 20 10:30:23 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 1... | [
"### 'YushiUeda/callhome\\_adapt\\_real'\n\n\nThis model was trained by YushiUeda using callhome recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Jun 20 10:30:23 EDT 2022'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7... | [
"TAGS\n#espnet #audio #diarization #dataset-callhome #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'YushiUeda/callhome\\_adapt\\_real'\n\n\nThis model was trained by YushiUeda using callhome recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\... |
text2text-generation | transformers |
## Article Title Generator
The model is based on the T5 language model and trained using a large collection of Medium articles.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("czearing/article-title-generator")
model = AutoModel.from_pretr... | {"license": "mit"} | czearing/article-title-generator | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T18:44:19+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Article Title Generator
The model is based on the T5 language model and trained using a large collection of Medium articles.
## Usage
Example code:
## License
MIT
| [
"## Article Title Generator\nThe model is based on the T5 language model and trained using a large collection of Medium articles.",
"## Usage\nExample code:",
"## License\nMIT"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Article Title Generator\nThe model is based on the T5 language model and trained using a large collection of Medium articles.",
"## Usage\nExample code:... |
fill-mask | transformers |
# ***astroBERT: a language model for astrophysics***
This public repository contains the work of the [NASA/ADS](https://ui.adsabs.harvard.edu/) on building an NLP language model tailored to astrophysics, along with tutorials and miscellaneous related files.
This model is **cased** (it treats `ads` and `ADS` different... | {"language": ["en"], "license": "mit", "task_categories": ["fill-mask"], "task_ids": ["masked-language-modeling"], "pipeline_tag": "fill-mask", "widget": [{"text": "M67 is one of the most studied [MASK] clusters.", "example_title": "M67"}, {"text": "A solar twin is a star with [MASK] parameters and chemical composition... | adsabs/astroBERT | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"pretraining",
"fill-mask",
"en",
"arxiv:2112.00590",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-28T19:17:48+00:00 | [
"2112.00590"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #pretraining #fill-mask #en #arxiv-2112.00590 #license-mit #endpoints_compatible #has_space #region-us
|
# *astroBERT: a language model for astrophysics*
This public repository contains the work of the NASA/ADS on building an NLP language model tailored to astrophysics, along with tutorials and miscellaneous related files.
This model is cased (it treats 'ads' and 'ADS' differently).
## astroBERT models
0. Base model: ... | [
"# *astroBERT: a language model for astrophysics*\nThis public repository contains the work of the NASA/ADS on building an NLP language model tailored to astrophysics, along with tutorials and miscellaneous related files. \nThis model is cased (it treats 'ads' and 'ADS' differently).",
"## astroBERT models\n0. Ba... | [
"TAGS\n#transformers #pytorch #safetensors #bert #pretraining #fill-mask #en #arxiv-2112.00590 #license-mit #endpoints_compatible #has_space #region-us \n",
"# *astroBERT: a language model for astrophysics*\nThis public repository contains the work of the NASA/ADS on building an NLP language model tailored to ast... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | rishiyoung/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T19:26:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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-uncased-finetuned-academic
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["elsevier-oa-cc-by"], "model-index": [{"name": "bert-base-uncased-finetuned-academic", "results": []}]} | egumasa/bert-base-uncased-finetuned-academic | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:elsevier-oa-cc-by",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T19:26:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-academic
====================================
This model is a fine-tuned version of bert-base-uncased on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5893
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 40\n* eval\\_batch\\_size: 40\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.97) and epsilon=0.0001\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n... |
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": ["f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "... | Neha2608/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 | 2022-06-28T19:29:01+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: 0.2207
* Accuracy is: 0.9185
* F1: 0.9185
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-125m-wikitext2
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the No... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "opt-125m-wikitext2", "results": []}]} | Aalaa/opt-125m-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T20:52:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| opt-125m-wikitext2
==================
This model is a fine-tuned version of facebook/opt-125m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3409
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #opt #text-generation #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n... |
fill-mask | transformers | ---
tags:
- generated_from_trainer
datasets:
- big_patent
model-index:
- name: reformer-finetuned
results: [] | {} | robingeibel/reformer-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"reformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T20:55:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #reformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ---
tags:
- generated_from_trainer
datasets:
- big_patent
model-index:
- name: reformer-finetuned
results: [] | [] | [
"TAGS\n#transformers #pytorch #tensorboard #reformer #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
## Story to Title
The model is based on the T5 language model and trained using a large collection of movie descriptions and corresponding titles. When given a story it will generate a corresponding title.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.fro... | {"license": "mit"} | czearing/story-to-title | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T21:35:19+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## Story to Title
The model is based on the T5 language model and trained using a large collection of movie descriptions and corresponding titles. When given a story it will generate a corresponding title.
## Usage
Example code:
## License
MIT
| [
"## Story to Title\nThe model is based on the T5 language model and trained using a large collection of movie descriptions and corresponding titles. When given a story it will generate a corresponding title.",
"## Usage\nExample code:",
"## License\nMIT"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Story to Title\nThe model is based on the T5 language model and trained using a large collection of movie descriptions and corresponding titles... |
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... | AdiKompella/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-28T22:05:08+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. -->
# twitter-roberta-base-CoNLL
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardif... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter-roberta-base-CoNLL", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args"... | emilys/twitter-roberta-base-CoNLL | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T22:07:52+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-CoNLL
==========================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0423
* Precision: 0.9531
* Recall: 0.9613
* F1: 0.9572
* Accuracy: 0.9926
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 1024\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Tra... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batc... |
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": ... | workRL/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-28T22:47:32+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/... | workRL/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-28T22:49:51+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. -->
# deberta-mlm-test
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3... | {"license": "mit", "tags": ["fill-mask", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-mlm-test", "results": []}]} | domenicrosati/deberta-mlm-test | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T22:53:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-mlm-test
================
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2792
* Accuracy: 0.4766
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\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/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-mrbeast/1656461472374/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-mrbeast | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-28T23:09:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & MrBeast
@elonmusk-mrbeast
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"
] |
fill-mask | transformers |
# DistilBERT base model (dummy test)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/mai... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | jdang/dummy-model | null | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-28T23:15:47+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #camembert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT base model (dummy test)
==================================
This model is a distilled version of the BERT base model. It was
introduced in this paper. The code for the distillation process can be found
here. This model is uncased: it does
not make a difference between english and English.
Model descriptio... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #camembert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is h... |
null | null |
# 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
... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | TinFernandez/dummy | null | [
"pytorch",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"region:us"
] | null | 2022-06-29T00:13:29+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#pytorch #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #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.
Disclaimer: The team rel... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#pytorch #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-xlsum-chinese-tradition
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/goog... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-xlsum-chinese-tradition", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type":... | elliotthwang/mt5-small-finetuned-xlsum-chinese-tradition | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-29T00:22:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-xlsum-chinese-tradition
===========================================
This model is a fine-tuned version of google/mt5-small on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 0.2578
* Rouge2: 0.0176
* Rougel: 0.2519
* Rougelsum: 0.2542
* Gen Len: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
null | null | # STTNet
Paper: Building Extraction from Remote Sensing Images with Sparse Token Transformers
1. Prepare Data
Prepare data for training, validation, and test phase. All images are with the resolution of $512 \times 512$. Please refer to the directory of **Data**.
For larger images, you can patch the image... | {} | KyanChen/BuildingExtraction | null | [
"has_space",
"region:us"
] | null | 2022-06-29T00:34:01+00:00 | [] | [] | TAGS
#has_space #region-us
| # STTNet
Paper: Building Extraction from Remote Sensing Images with Sparse Token Transformers
1. Prepare Data
Prepare data for training, validation, and test phase. All images are with the resolution of $512 \times 512$. Please refer to the directory of Data.
For larger images, you can patch the images wi... | [
"# STTNet\nPaper: Building Extraction from Remote Sensing Images with Sparse Token Transformers\n1. Prepare Data \n Prepare data for training, validation, and test phase. All images are with the resolution of $512 \\times 512$. Please refer to the directory of Data.\n \n For larger images, you can patch th... | [
"TAGS\n#has_space #region-us \n",
"# STTNet\nPaper: Building Extraction from Remote Sensing Images with Sparse Token Transformers\n1. Prepare Data \n Prepare data for training, validation, and test phase. All images are with the resolution of $512 \\times 512$. Please refer to the directory of Data.\n \n ... |
text-to-speech | nemo | # NVIDIA Hifigan Vocoder (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![Language](http... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["text-to-speech", "speech", "audio", "Vocoder", "GAN", "pytorch", "NeMo", "Riva"], "datasets": ["ljspeech"]} | nvidia/tts_hifigan | null | [
"nemo",
"text-to-speech",
"speech",
"audio",
"Vocoder",
"GAN",
"pytorch",
"NeMo",
"Riva",
"en",
"dataset:ljspeech",
"arxiv:2010.05646",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-06-29T00:51:43+00:00 | [
"2010.05646"
] | [
"en"
] | TAGS
#nemo #text-to-speech #speech #audio #Vocoder #GAN #pytorch #NeMo #Riva #en #dataset-ljspeech #arxiv-2010.05646 #license-cc-by-4.0 #has_space #region-us
| # NVIDIA Hifigan Vocoder (en-US)
<style>
img {
display: inline;
}
</style>
| 
| 
| 
|  |
HiFiGAN [1] is a generative adversarial network (GAN) model that generates audio fr... | [
"# NVIDIA Hifigan Vocoder (en-US)\n<style>\nimg {\n display: inline;\n}\n</style>\n| \n| \n| \n|  |\n\nHiFiGAN [1] is a generative adversarial network (GAN) model that ge... | [
"TAGS\n#nemo #text-to-speech #speech #audio #Vocoder #GAN #pytorch #NeMo #Riva #en #dataset-ljspeech #arxiv-2010.05646 #license-cc-by-4.0 #has_space #region-us \n",
"# NVIDIA Hifigan Vocoder (en-US)\n<style>\nimg {\n display: inline;\n}\n</style>\n| \n| ![Model size](#mod... |
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... | okite97/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 | 2022-06-29T00:53:59+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: 0.1659
* Accuracy: 0.9325
* F1: 0.9328
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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-nsc-final_2-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-nsc-final_2-google-colab", "results": []}]} | YuanWellspring/wav2vec2-nsc-final_2-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-29T01:59:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-nsc-final_2-google-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training... | [
"# wav2vec2-nsc-final_2-google-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-nsc-final_2-google-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n... |
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