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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_fold_6_ternary
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_6_ternary", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_6_ternary | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-31T23:27:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_6\_ternary
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6625
* F1: 0.7588
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | dfsj/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-31T23:46:22+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3163
* Accuracy: 0.9448
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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:... |
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/1071329495565529088/yyYo... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ravikiranprao/1659316650453/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/ravikiranprao | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T00:16:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ravikiran P Rao
@ravikiranprao
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
--... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "m... | reachrkr/Cartpole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T01:16:50+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | reachrkr/Reinforce-Cartpole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T01:18:48+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcemen... |
question-answering | transformers |
# roberta-base for QA finetuned over community safety domain data
We fine-tuned the roBERTa-based model (https://huggingface.co/deepset/roberta-base-squad2) over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering.
### Using model in Transformers
```python... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"], "model-index": [{"name": "plm_qa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "config": "squad_v2", "split": "validation"}, "metrics": [{"type": "exact_match",... | yirenl2/plm_qa | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T02:06:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us
|
# roberta-base for QA finetuned over community safety domain data
We fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering.
### Using model in Transformers
| [
"# roberta-base for QA finetuned over community safety domain data\n\nWe fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering.",
"### Using model in Transformers"
] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #model-index #endpoints_compatible #region-us \n",
"# roberta-base for QA finetuned over community safety domain data\n\nWe fine-tuned the roBERTa-based model (URL over LiveSafe community safety dialogue data for e... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixelcopter-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter... | reachrkr/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T02:45:57+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of ... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dit-base-finetuned-rvlcdip-finetuned-eurosat
This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https:... | {"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "dit-base-finetuned-rvlcdip-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "... | keithanpai/dit-base-finetuned-rvlcdip-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T03:30:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us
| dit-base-finetuned-rvlcdip-finetuned-eurosat
============================================
This model is a fine-tuned version of microsoft/dit-base-finetuned-rvlcdip on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7997
* Accuracy: 0.7315
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* ... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vc-bantai-vit-withoutAMBI-adunest-v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vc-bantai-vit-withoutAMBI-adunest-v2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefol... | AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T03:42:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vc-bantai-vit-withoutAMBI-adunest-v2
====================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8271
* Accuracy: 0.7705
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 200\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #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* learnin... |
text-generation | transformers |
# Lyem Ningthou DialoGPT Model | {"tags": ["conversational"]} | Lyem/LyemBotv1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T04:36:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Lyem Ningthou DialoGPT Model | [
"# Lyem Ningthou DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Lyem Ningthou DialoGPT Model"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pong-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{... | reachrkr/Reinforce-Pong-PLE-v0 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T05:01:48+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-generation | transformers |
# Bloom Model finetuned on the NCBI disease dataset to generate synthetic data similar to the NCBI disease dataset. | {"language": "en", "tags": ["text-generation"], "datasets": ["ncbi_disease"]} | leslyarun/bloom_ncbi_finetuned | null | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"en",
"dataset:ncbi_disease",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T05:25:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bloom #text-generation #en #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bloom Model finetuned on the NCBI disease dataset to generate synthetic data similar to the NCBI disease dataset. | [
"# Bloom Model finetuned on the NCBI disease dataset to generate synthetic data similar to the NCBI disease dataset."
] | [
"TAGS\n#transformers #pytorch #safetensors #bloom #text-generation #en #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bloom Model finetuned on the NCBI disease dataset to generate synthetic data similar to the NCBI disease dataset."
] |
summarization | transformers |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params |... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | maan909/unisumm | null | [
"transformers",
"jax",
"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T05:32:51+00:00 | [] | [
"en"
] | TAGS
#transformers #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for DistilBART models
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for DistilBART models"
] | [
"TAGS\n#transformers #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART d... |
reinforcement-learning | stable-baselines3 | # "Beyko7/ppo-LunarLander-v2"
This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip i... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | BekirTaha/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-08-01T05:40:27+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
| # "Beyko7/ppo-LunarLander-v2"
This is a pre-trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
Then, you can use the model like this:
### Evaluati... | [
"# \"Beyko7/ppo-LunarLander-v2\"\n\nThis is a pre-trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the model like thi... | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"# \"Beyko7/ppo-LunarLander-v2\"\n\nThis is a pre-trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when 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/1122432883036172288/mYZ4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/kantegory/1659338795219/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/kantegory | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T06:26:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
David Dobryakov
@kantegory
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. -->
# enlm-r
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following result... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "enlm-r", "results": []}]} | manirai91/enlm-r | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T06:58:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| enlm-r
======
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4837
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0006\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 128\n* total\\_train\\_batch\\_size: ... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0006\n* train\\_batch\\_size: 16\n* eval\\_ba... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | meln1k/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-01T07:03:39+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\n This is a trained model of a A2C agent playing AntBulletEnv-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\n This is a trained model of a A2C agent playing AntBulletEnv-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO:... |
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. -->
# twitch-league-roberta-base-test
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "twitch-league-roberta-base-test", "results": []}]} | Epidot/twitch-league-roberta-base-test | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T07:28:38+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# twitch-league-roberta-base-test
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# twitch-league-roberta-base-test\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# twitch-league-roberta-base-test\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses &... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | JamesSantosxx/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T07:30:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
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. -->
# ner_hindi_bert
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingua... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "model-index": [{"name": "ner_hindi_bert", "results": []}]} | lakshaywadhwa1993/ner_hindi_bert | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T08:05:27+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ner\_hindi\_bert
================
This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3713
* Overall Precision: 0.8942
* Overall Recall: 0.8972
* Overall F1: 0.8957
* Overall Accuracy: 0.9367
* Loc F1: 0.8766
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #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\\_ba... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | AntioxVII/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-01T08:26:08+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
translation | transformers |
## [t5-small](https://huggingface.co/t5-small) exported to the ONNX format and dynamically quantized.
## Model description
[T5](https://huggingface.co/docs/transformers/model_doc/t5#t5) is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is co... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["int8", "summarization", "translation"], "datasets": ["c4"]} | echarlaix/t5-small-int8-dynamic | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"int8",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T08:33:10+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | TAGS
#transformers #onnx #t5 #text2text-generation #int8 #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## t5-small exported to the ONNX format and dynamically quantized.
## Model description
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
For more information, please take a look at the original p... | [
"## t5-small exported to the ONNX format and dynamically quantized.",
"## Model description\n\nT5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.\n\nFor more information, please take a look at the... | [
"TAGS\n#transformers #onnx #t5 #text2text-generation #int8 #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## t5-small exported to the ONNX format and dynamically qua... |
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. -->
# Tarkan/distilbert-base-uncased-finetuned-ner
SA yakında silicem bunu xd
This model is a fine-tuned version of [distilbert-base-uncased]... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Tarkan/distilbert-base-uncased-finetuned-ner", "results": []}]} | Tarkan/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T08:51:39+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Tarkan/distilbert-base-uncased-finetuned-ner
============================================
SA yakında silicem bunu xd
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: 0.0588
* Validation Loss: 0.0735
* Train Pr... | [
"### 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': 678, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #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': 'AdamWeightD... |
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. -->
# 20split_dataset_version4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "20split_dataset_version4", "results": []}]} | Billwzl/20split_dataset_version4 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T08:52:20+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| 20split\_dataset\_version4
==========================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1060
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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval... |
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-ner-wnut
This model is a fine-tuned version of [distilbert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-base-uncased-finetuned-ner-wnut", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-ner-wnut | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T09:37:08+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-base-uncased-finetuned-ner-wnut
=========================================================
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: 0.1241
* Validation Loss: 0.3433
* Train Pre... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'Adam', 'config': {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 636, 'end\\_l... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #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: {'inner\\_optimizer':... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetahBulletEnv-v0... | meln1k/a2c-HalfCheetahBulletEnv-v0 | null | [
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-01T10:19:36+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing HalfCheetahBulletEnv-v0
This is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing HalfCheetahBulletEnv-v0\n This is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing HalfCheetahBulletEnv-v0\n This is a trained model of a A2C agent playing HalfCheetahBulletEnv-v0 using the stable-baselines3 library.\n \n ## Usage (with Sta... |
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-ft750_reg3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft750_reg3", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft750_reg3 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T10:22:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft750\_reg3
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6143
* Mse: 0.6143
* Mae: 0.6022
* R2: 0.4218
* Accuracy: 0.52
Model d... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-cnn_full-adafactor-bs6
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn_full-adafactor-b... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn_full-adafactor-bs6", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailyma... | oMateos2020/pegasus-newsroom-cnn_full-adafactor-bs6 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-01T10:22:51+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| pegasus-newsroom-cnn\_full-adafactor-bs6
========================================
This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn\_full-adafactor-bs6 on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8671
* Rouge1: 44.1026
* Rouge2: 21.4261
* Ro... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-0... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | rdruce/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-01T10:33:05+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft750_reg4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft750_reg4", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft750_reg4 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T10:56:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft750\_reg4
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7393
* Mse: 0.7393
* Mae: 0.6578
* R2: 0.3041
* Accuracy: 0.4733
Model... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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="Beyko7/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | BekirTaha/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-01T11:01:10+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | turhancan97/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-01T11:07:56+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
sentence-similarity | sentence-transformers |
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: RobertaModel
(1) Pooling mean
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) instal... | {"license": ["cc-by-sa-4.0"], "tags": ["sentence-transformers", "causal-lm"], "pipeline_tag": "sentence-similarity"} | Inkdrop/gpl | null | [
"sentence-transformers",
"pytorch",
"causal-lm",
"sentence-similarity",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T11:12:11+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #causal-lm #sentence-similarity #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# TODO: Name of Model
TODO: Description
## Model Description
TODO: Add relevant content
(0) Base Transformer Type: RobertaModel
(1) Pooling mean
## Usage (Sentence-Transformers)
Using this model becomes more convenient when you have sentence-transformers installed:
Then you can use the model like this:
#... | [
"# TODO: Name of Model\n\nTODO: Description",
"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: RobertaModel\n\n(1) Pooling mean",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\n\n\nThen you can use th... | [
"TAGS\n#sentence-transformers #pytorch #causal-lm #sentence-similarity #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# TODO: Name of Model\n\nTODO: Description",
"## Model Description\nTODO: Add relevant content\n\n(0) Base Transformer Type: RobertaModel\n\n(1) Pooling mean",
"## Usage (Sentenc... |
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="Beyko7/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | BekirTaha/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-01T11:16:19+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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... | Keneston/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T11:26:03+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: 0.2214
* Accuracy: 0.9275
* F1: 0.9274
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# News_Sentiment_Analysis
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://hugging... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "News_Sentiment_Analysis", "results": []}]} | shashanksrinath/News_Sentiment_Analysis | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-01T12:01:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# News_Sentiment_Analysis
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# News_Sentiment_Analysis\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# News_Sentiment_Analysis\n\nThis model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.",
"#... |
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-ru-finetuned-en-to-ru-Legal
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ru-finetuned-en-to-ru-Legal", "results": []}]} | Kovalev/opus-mt-en-ru-finetuned-en-to-ru-Legal | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ru-finetuned-en-to-ru-Legal
======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ru on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8561
* Bleu: 46.7284
* Gen Len: 23.1317
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
null | keras | random text here | {} | qmjnh/FLowerCLassification-model | null | [
"keras",
"has_space",
"region:us"
] | null | 2022-08-01T12:11:01+00:00 | [] | [] | TAGS
#keras #has_space #region-us
| random text here | [] | [
"TAGS\n#keras #has_space #region-us \n"
] |
text-classification | transformers |
# Model Card for FEVER Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrieval... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-reranker-fever | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"information retrieval",
"reranking",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:13:21+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for FEVER Reranker in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
>
>It has been previously established that results from initial retrieval... | [
"# Model Card for FEVER Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n> \n>It has been previously established that results from initia... | [
"TAGS\n#transformers #pytorch #bert #text-classification #information retrieval #reranking #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for FEVER Reranker in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information... |
feature-extraction | transformers |
# Model Card for FEVER Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-qry-encoder-fever | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:18:05+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for FEVER Question Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Inferen... | [
"# Model Card for FEVER Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Evalu... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for FEVER Question Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) comp... |
null | transformers |
# Model Card for FEVER Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="https://github.com/IBM/kgi-slot-filling/raw/re2g/model_c... | {"license": "apache-2.0", "tags": ["information retrieval", "reranking"]} | ibm/re2g-ctx-encoder-fever | null | [
"transformers",
"pytorch",
"dpr",
"information retrieval",
"reranking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:23:51+00:00 | [] | [] | TAGS
#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for FEVER Context Encoder in Re2G
# Model Details
> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output.
<img src="URL width="100%">
## Training, Evaluation and Inferenc... | [
"# Model Card for FEVER Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further train it end-to-end through its impact in generating the correct output. \n\n<img src=\"URL width=\"100%\">",
"## Training, Evalua... | [
"TAGS\n#transformers #pytorch #dpr #information retrieval #reranking #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for FEVER Context Encoder in Re2G",
"# Model Details\n\n> The approach of RAG, Multi-DPR, and KGI is to train a neural IR (Information Retrieval) component and further tra... |
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003
This model is a fine-tuned version of [distilbert-base-uncased](https:/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003 | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:42:27+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003
==============================================================
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: 0.0516
* Validation Loss: 0.0592
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #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: {'inner\\_optimizer':... |
text-classification | transformers | <strong>Classifier of topic discussed in vaccine-related content in Italian language</strong></br>
A monolingual model for classifying the topic discussed in vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts, Twitte... | {"license": "mit"} | brema76/vaccine_topic_it | null | [
"transformers",
"tf",
"bert",
"text-classification",
"arxiv:2207.12264",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:54:38+00:00 | [
"2207.12264"
] | [] | TAGS
#transformers #tf #bert #text-classification #arxiv-2207.12264 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| <strong>Classifier of topic discussed in vaccine-related content in Italian language</strong></br>
A monolingual model for classifying the topic discussed in vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts, Twitte... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #arxiv-2207.12264 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft750_reg5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft750_reg5", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft750_reg5 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T12:57:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft750\_reg5
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6298
* Mse: 0.6298
* Mae: 0.6087
* R2: 0.4072
* Accuracy: 0.4973
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "m... | turhancan97/Reinforce-1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T13:02:43+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
feature-extraction | transformers | # An alias of [relbert/relbert-roberta-large-nce-semeval2012-0-400](https://huggingface.co/relbert/relbert-roberta-large-nce-semeval2012-0-400) | {} | relbert/relbert-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-01T13:09:25+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #has_space #region-us
| # An alias of relbert/relbert-roberta-large-nce-semeval2012-0-400 | [
"# An alias of relbert/relbert-roberta-large-nce-semeval2012-0-400"
] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #has_space #region-us \n",
"# An alias of relbert/relbert-roberta-large-nce-semeval2012-0-400"
] |
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. -->
# v2-fine-tune-wav2vec2-Vietnamese-ARS-demo
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](h... | {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "v2-fine-tune-wav2vec2-Vietnamese-ARS-demo", "results": []}]} | thocheat/v2-fine-tune-wav2vec2-Vietnamese-ARS-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T13:23:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| v2-fine-tune-wav2vec2-Vietnamese-ARS-demo
=========================================
This model is a fine-tuned version of nguyenvulebinh/wav2vec2-base-vietnamese-250h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2515
* Wer: 0.2235
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size:... |
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. -->
# pegassus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_d... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegassus-samsum", "results": []}]} | liujxing/pegassus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T13:37:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegassus-samsum
===============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5463
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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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\\... |
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-cowese-base-es
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model descri... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-cowese-base-es", "results": []}]} | plncmm/deberta-clinical-scratch-cowese-es | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T13:53:15+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-cowese-base-es
This model is a fine-tuned version of [](URL 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 fol... | [
"# deberta-cowese-base-es\n\nThis model is a fine-tuned version of [](URL 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",
"### Tra... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-cowese-base-es\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & lim... |
text-generation | transformers |
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました
ファインチューニングには、内定者の二万件以上のESを用いました。
webアプリ<br>
https://huranokuma-es-app-9t34vl.streamlitapp.com/ <br>
http://www.eswri... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "thumbnail": "https://1.bp.blogspot.com/-pOL-P7Mvgkg/YEGQAdidksI/AAAAAAABdc0/SbD0lC_X8iY_t5xLFtQYFC3FHFgziBuzgCNcBGAsYHQ/s932/buranko_businesswoman_sad.png", "widget": [{"text": "\u5fa1\u793e\u3092\u5fd7\u671b\u3057... | huranokuma/es | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T13:59:47+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ja #japanese #lm #nlp #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
!thumbnail
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました
ファインチューニングには、内定者の二万件以上のESを用いました。
webアプリ<br>
URL <br>
URL
The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd. | [
"# ESを書くAI\nJapanese GPT-2 modelをファインチューニングしました\nファインチューニングには、内定者の二万件以上のESを用いました。\n\nwebアプリ<br>\nURL <br>\nURL\n\nThe model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ja #japanese #lm #nlp #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ESを書くAI\nJapanese GPT-2 modelをファインチューニングしました\nファインチューニングには、内定者の二万件以上のESを用いました。\n\nwebアプリ<br>\nURL <br>\nURL\n\nThe model was trained us... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | Forkits/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T14:15:48+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
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-small-ipadic_bpe
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the fol... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-small-ipadic_bpe", "results": []}]} | schnell/bert-small-ipadic_bpe | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T14:40:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-small-ipadic\_bpe
======================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6777
* Accuracy: 0.6519
Model description
-----------------
More information needed
Intended uses & limitations
-------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* total\\_train\\_batch\\_size: 768\n* total\\_eval\\_batch\\_size: 24\n... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_... |
automatic-speech-recognition | transformers | Tokenizer created incorrectly: filtering of chars not performed.
Corrected model at https://huggingface.co/sanchit-gandhi/flax-wav2vec2-ctc-spgispeech-baseline-cased | {} | sanchit-gandhi/flax-wav2vec2-ctc-spgispeech-cased | null | [
"transformers",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T14:48:52+00:00 | [] | [] | TAGS
#transformers #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Tokenizer created incorrectly: filtering of chars not performed.
Corrected model at URL | [] | [
"TAGS\n#transformers #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# silviacamplani/twitter-roberta-base-finetuned-ner-wnut
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https:/... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/twitter-roberta-base-finetuned-ner-wnut", "results": []}]} | silviacamplani/twitter-roberta-base-finetuned-ner-wnut | null | [
"transformers",
"tf",
"tensorboard",
"roberta",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T14:50:19+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #roberta #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/twitter-roberta-base-finetuned-ner-wnut
======================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0812
* Validation Loss: 0.2553
* Train P... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'Adam', 'config': {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 636, 'end\\_l... | [
"TAGS\n#transformers #tf #tensorboard #roberta #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: {'inner\\_optimizer': {'class\\_name': 'Adam... |
text-classification | transformers | ## How to use this model
Load files
Run commands
| {"language": "fr", "license": "mit", "datasets": ["oscar"]} | bdoohan-goog/dummy-model2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"fr",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T15:10:07+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #bert #text-classification #fr #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## How to use this model
Load files
Run commands
| [
"## How to use this model\n\nLoad files\nRun commands"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #fr #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## How to use this model\n\nLoad files\nRun commands"
] |
text-classification | transformers |
# Dynamically quantized DistilBERT base uncased finetuned SST-2
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetune... | {"language": "en", "license": "apache-2.0", "tags": ["text-classification", "neural-compressor", "int8", "8-bit"], "datasets": ["sst2", "glue"], "metrics": ["accuracy"]} | echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"neural-compressor",
"int8",
"8-bit",
"en",
"dataset:sst2",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T15:30:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #8-bit #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Dynamically quantized DistilBERT base uncased finetuned SST-2
## Table of Contents
- Model Details
- How to Get Started With the Model
## Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compressor.
- Model T... | [
"# Dynamically quantized DistilBERT base uncased finetuned SST-2",
"## Table of Contents\n- Model Details\n- How to Get Started With the Model",
"## Model Details\nModel Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compre... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #8-bit #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Dynamically quantized DistilBERT base uncased finetuned SST-2",
"## Table of Contents\n- Model Details... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "m... | turhancan97/Reinforce-2 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-01T15:44:23+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | aemili/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T16:05:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7578
* Matthews Correlation: 0.5317
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# Lyem Ningthou DialoGPT Model | {"tags": ["conversational"]} | Lyem/LyemBotv2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T17:17:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Lyem Ningthou DialoGPT Model | [
"# Lyem Ningthou DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Lyem Ningthou DialoGPT Model"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-finetuned-pile-of-law-tos
This model is a MLM fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-tiny-finetuned-pile-of-law-tos", "results": []}]} | muhtasham/bert-tiny-finetuned-pile-of-law-tos | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T17:22:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-pile-of-law-tos
===================================
This model is a MLM fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the pile-of-law/tos dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3545
Model description
-----------------
More information nee... | [
"### 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: 15",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-fashion-mnist-quality-drift
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microso... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fashion_mnist_quality_drift"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "resnet-50-fashion-mnist-quality-drift", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "fashion_... | arize-ai/resnet-50-fashion-mnist-quality-drift | null | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:fashion_mnist_quality_drift",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T18:32:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fashion_mnist_quality_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| resnet-50-fashion-mnist-quality-drift
=====================================
This model is a fine-tuned version of microsoft/resnet-50 on the fashion\_mnist\_quality\_drift dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7473
* Accuracy: 0.73
* F1: 0.7289
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-fashion_mnist_quality_drift #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
text2text-generation | transformers | ### marian-mt-pcm-en
* source language: pcm (Nigerian Pidgin)
* target language: en (English)
* dataset: Parallel Sentences from the Pidgin and message translations (English) of the Bible.
* model: transformer-align
* pre-processing: normalization + SentencePiece
## Performance
| test set | BLEU |
|---... | {"license": "mit"} | Enutrof/marian-mt-pcm-en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T18:34:22+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### marian-mt-pcm-en
* source language: pcm (Nigerian Pidgin)
* target language: en (English)
* dataset: Parallel Sentences from the Pidgin and message translations (English) of the Bible.
* model: transformer-align
* pre-processing: normalization + SentencePiece
Performance
-----------
| [
"### marian-mt-pcm-en\n\n\n* source language: pcm (Nigerian Pidgin)\n* target language: en (English)\n* dataset: Parallel Sentences from the Pidgin and message translations (English) of the Bible.\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n\n\nPerformance\n-----------"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### marian-mt-pcm-en\n\n\n* source language: pcm (Nigerian Pidgin)\n* target language: en (English)\n* dataset: Parallel Sentences from the Pidgin and message translations (English... |
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. -->
# mal_tls-bert-base-relu
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the follow... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal_tls-bert-base-relu", "results": []}]} | SharpAI/mal-tls-bert-base-relu | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T18:53:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal_tls-bert-base-relu
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
"# mal_tls-bert-base-relu\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore ... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal_tls-bert-base-relu\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"##... |
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. -->
# mal_tls-bert-base-relu-w8a8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalua... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal_tls-bert-base-relu-w8a8", "results": []}]} | SharpAI/mal-tls-bert-base-relu-w8a8 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T19:22:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal_tls-bert-base-relu-w8a8
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
## Tra... | [
"# mal_tls-bert-base-relu-w8a8\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 info... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal_tls-bert-base-relu-w8a8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-bert-finetuned-cuad
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "tiny-bert-finetuned-cuad", "results": []}]} | muhtasham/bert-tiny-finetuned-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T19:34:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-cuad #license-apache-2.0 #endpoints_compatible #region-us
| tiny-bert-finetuned-cuad
========================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the portion of cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4606
Note
====
The model was not trained on the whole dataset but, the first 10% of 'tr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1024\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",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-cuad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_bat... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | mrm8488/pyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-01T19:36:37+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
```
```
prompt = """informal english: corn fields are all across il... | {} | BigSalmon/InformalToFormalLincoln60Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T19:53:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# turkishReviews_5_topic
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves th... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "turkishReviews_5_topic", "results": []}]} | cansen88/turkishReviews_5_topic | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T20:21:12+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| turkishReviews\_5\_topic
========================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 6.8939
* Validation Loss: 6.8949
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay... |
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-kit-TLDR_30
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves th... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-kit-TLDR_30", "results": []}]} | CennetOguz/gpt2-kit-TLDR_30 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T21:15:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-kit-TLDR\_30
=================
This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6706
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* ... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mal_tls-mobilebert
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following ... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal_tls-mobilebert", "results": []}]} | SharpAI/mal-tls-mobilebert | null | [
"transformers",
"pytorch",
"tf",
"mobilebert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T21:45:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #mobilebert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal_tls-mobilebert
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tra... | [
"# mal_tls-mobilebert\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore info... | [
"TAGS\n#transformers #pytorch #tf #mobilebert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal_tls-mobilebert\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"... |
null | null | 
## inference
```python
``` | {} | SauronLee/BiLSTM_Finding_NLP_Papers | null | [
"region:us"
] | null | 2022-08-01T22:12:41+00:00 | [] | [] | TAGS
#region-us
| 
## inference
| [
"## inference"
] | [
"TAGS\n#region-us \n",
"## inference"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-finetuned-xglue-ner
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xglue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-tiny-finetuned-xglue-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xglue", "type":... | muhtasham/bert-tiny-finetuned-xglue-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:xglue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T22:13:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-xglue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-xglue-ner
=============================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the xglue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2489
* Precision: 0.6308
* Recall: 0.6681
* F1: 0.6489
* Accuracy: 0.9274
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-xglue #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\\_r... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-finetuned-wnut17-ner
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wnut_17"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-tiny-finetuned-wnut17-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wnut_17", "t... | muhtasham/bert-tiny-finetuned-wnut17-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T22:24:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-wnut17-ner
==============================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the wnut\_17 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6054
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.8961
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wnut_17 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-generation | transformers |
#Artoria Bot draft | {"tags": ["conversational"]} | Ironpanther1/ArtoriaBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-01T22:37:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Artoria Bot draft | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-finetuned-glue-rte
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/go... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-tiny-finetuned-glue-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "rte", "split":... | muhtasham/bert-tiny-finetuned-glue-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T22:42:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-glue-rte
============================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6673
* Accuracy: 0.6318
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.4294744851376705e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 16\n* seed: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #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* l... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-cheese-32
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingf... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "imagefolder", "metrics": []} | rdruce/ddpm-cheese-32 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-01T23:05:54+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-imagefolder #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-cheese-32
## Model description
This diffusion model is trained with the Diffusers library
on the 'imagefolder' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the... | [
"# ddpm-cheese-32",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'imagefolder' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training ... | [
"TAGS\n#diffusers #tensorboard #en #dataset-imagefolder #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-cheese-32",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'imagefolder' dataset.",
"## Intended uses & limitations",
"#### How to use",
... |
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. -->
# albert-base-v2-finetuned-wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "wnli", "split... | jinghan/albert-base-v2-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T23:16:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-finetuned-wnli
=============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6981
* Accuracy: 0.5634
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #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\\_r... |
text-classification | transformers |
# XLM-T-Sent-Politics
This is an "extension" of the multilingual `twitter-xlm-roberta-base-sentiment` model ([model](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment), [original paper](https://arxiv.org/abs/2104.12250)) with a focus on sentiment from politicians' tweets. The original sentiment fin... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "XLM-T-Sent-Politics", "results": []}]} | cardiffnlp/xlm-twitter-politics-sentiment | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"generated_from_keras_callback",
"arxiv:2104.12250",
"arxiv:2202.00396",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-01T23:34:22+00:00 | [
"2104.12250",
"2202.00396"
] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #generated_from_keras_callback #arxiv-2104.12250 #arxiv-2202.00396 #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-T-Sent-Politics
This is an "extension" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (model, original paper) with a focus on sentiment from politicians' tweets. The original sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but further training was done using tweets... | [
"# XLM-T-Sent-Politics\n\nThis is an \"extension\" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (model, original paper) with a focus on sentiment from politicians' tweets. The original sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but further training was done usin... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #generated_from_keras_callback #arxiv-2104.12250 #arxiv-2202.00396 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-T-Sent-Politics\n\nThis is an \"extension\" of the multilingual 'twitter-xlm-roberta-base-sentiment' model (mode... |
question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/PruebaBert"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question': ... | {"language": "en", "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model... | JAlexis/bert001 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T00:35:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #endpoints_compatible #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
| [
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #endpoints_compatible #region-us \n",
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] |
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... | skr1125/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T00:50:37+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: 0.1343
* F1: 0.8637
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers | To be completed. | {} | orestxherija/roberta-base-adr-smm4h2022 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T01:23:46+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| To be completed. | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1009932396333031424/8FzK... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/itsjefftiedrich/1659408624518/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/itsjefftiedrich | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T01:48:45+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jeff Tiedrich
@itsjefftiedrich
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"
] |
question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/JAlexis/bert003"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'questi... | {"language": "en", "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model... | JAlexis/bert003 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T02:00:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #endpoints_compatible #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
| [
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #en #endpoints_compatible #region-us \n",
"## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.",
"## How to use"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es-Resumen-2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/googl... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es-Resumen-2", "results": []}]} | OMARS200/mt5-small-finetuned-amazon-en-es-Resumen-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T02:09:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es-Resumen-2
==========================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0910
* Rouge1: 16.3799
* Rouge2: 7.6088
* Rougel: 15.9886
* Rougelsum: 16.1691
Model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
null | null | from keras.models import load_model
model = load_model('/content/gdrive/My Drive/Colab Notebooks/model.h5', custom_objects={'TFBertModel': transformers.TFBertModel})
def check_similarity(sentence1, sentence2):
sentence_pairs = np.array([[str(sentence1), str(sentence2)]])
test_data = BertSemanticDataGenerator... | {"license": "apache-2.0"} | liamh03/bratwurst | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-02T03:04:13+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| from URL import load_model
model = load_model('/content/gdrive/My Drive/Colab Notebooks/model.h5', custom_objects={'TFBertModel': transformers.TFBertModel})
def check_similarity(sentence1, sentence2):
sentence_pairs = URL([[str(sentence1), str(sentence2)]])
test_data = BertSemanticDataGenerator(
sent... | [] | [
"TAGS\n#license-apache-2.0 #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. -->
# phobert-base-finetuned-law
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base)... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "phobert-base-finetuned-law", "results": []}]} | dontpencil/phobert-base-finetuned-law | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T03:05:41+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# phobert-base-finetuned-law
This model is a fine-tuned version of vinai/phobert-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 hyperparam... | [
"# phobert-base-finetuned-law\n\nThis model is a fine-tuned version of vinai/phobert-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",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# phobert-base-finetuned-law\n\nThis model is a fine-tuned version of vinai/phobert-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# croupier-creature-classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/go... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "widget": [{"src": "https://huggingface.co/alkzar90/croupier-creature-classifier/resolve/main/examples/crusader_peco_peco.png", "example_title": "Crusader-Rangarok-Online"}, {"src"... | alkzar90/croupier-creature-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T04:24:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| croupier-creature-classifier
============================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the croupier-mtg-dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7583
* Accuracy: 0.7471
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PV-Bio_clinicalBERT-superset
This model is a fine-tuned version of [giacomomiolo/electramed_base_scivocab_1M](https://huggingfac... | {"tags": ["generated_from_trainer"], "datasets": ["pv_dataset"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "PV-Bio_clinicalBERT-superset", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "pv_dataset", "type": "pv_dataset", "... | commanderstrife/PV-Bio_clinicalBERT-superset | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:pv_dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T04:36:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-pv_dataset #model-index #autotrain_compatible #endpoints_compatible #region-us
| PV-Bio\_clinicalBERT-superset
=============================
This model is a fine-tuned version of giacomomiolo/electramed\_base\_scivocab\_1M on the pv\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2082
* Precision: 0.7056
* Recall: 0.7474
* F1: 0.7259
* Accuracy: 0.9657
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #dataset-pv_dataset #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-common1000asli-demo-colab-dd
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https:/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-common1000asli-demo-colab-dd", "results": []}]} | voice/wav2vec2-large-xlsr-common1000asli-demo-colab-dd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T04:56:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-common1000asli-demo-colab-dd
================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0671
* Wer: 0.5268
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
fill-mask | transformers | VarzeshiBERT:
Introducing a language model based on Bret to analyze sports content in Persian language
Introduction:
VarzeshiBERT language model is presented for the purpose of Persian sports analysis in topics related to this linguistic field | {"widget": [{"text": "\u06cc\u0648\u0633\u06cc\u0646 \u0628\u0648\u0644\u062a \u062f\u0648\u0646\u062f\u0647\u0654 [MASK] \u062f\u0648 \u0633\u0631\u0639\u062a \u0648 \u0633\u0631\u06cc\u0639\u062a\u0631\u06cc\u0646 \u0627\u0646\u0633\u0627\u0646 \u062c\u0647\u0627\u0646 \u0627\u0633\u062a.", "example_title": "EXAMPLE1... | montazeri/bert-base-persian-sport-bert-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T05:07:47+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| VarzeshiBERT:
Introducing a language model based on Bret to analyze sports content in Persian language
Introduction:
VarzeshiBERT language model is presented for the purpose of Persian sports analysis in topics related to this linguistic field | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | kyoumiaoi/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T05:15:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5499
* Wer: 0.3435
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-korean-demo-with-LM
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-korean-demo-with-LM", "results": []}]} | NX2411/wav2vec2-large-xlsr-korean-demo-with-LM | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T05:24:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-korean-demo-with-LM
=======================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3015
* Wer: 0.2113
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4... |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1209845735
- CO2 Emissions (in grams): 3602.3174
## Validation Metrics
- Loss: 2.484
- Rouge1: 38.448
- Rouge2: 10.900
- RougeL: 22.080
- RougeLsum: 33.458
- Gen Len: 115.982
## Usage
You can use cURL to access this model:
```
$ curl -X PO... | {"language": ["unk"], "tags": ["autotrain", "summarization"], "datasets": ["Jacobsith/autotrain-data-Hello_there"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 3602.3174355473616}, "model-index": [{"name": "Jacobsith/autotrain-Hello_there-1209845735", "results": [{"task": {"... | Jacobsith/autotrain-Hello_there-1209845735 | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:Jacobsith/autotrain-data-Hello_there",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T05:38:58+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-Jacobsith/autotrain-data-Hello_there #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1209845735
- CO2 Emissions (in grams): 3602.3174
## Validation Metrics
- Loss: 2.484
- Rouge1: 38.448
- Rouge2: 10.900
- RougeL: 22.080
- RougeLsum: 33.458
- Gen Len: 115.982
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1209845735\n- CO2 Emissions (in grams): 3602.3174",
"## Validation Metrics\n\n- Loss: 2.484\n- Rouge1: 38.448\n- Rouge2: 10.900\n- RougeL: 22.080\n- RougeLsum: 33.458\n- Gen Len: 115.982",
"## Usage\n\nYou can use cURL to access this... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-Jacobsith/autotrain-data-Hello_there #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1209845... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | DrY/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T06:52:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type... | Chandanab/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T07:37:21+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1677
* Accuracy: 0.9394
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5... |
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-base-finetuned-wnli
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glu... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "wnli", "split": "train... | jinghan/roberta-base-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T07:49:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-wnli
===========================
This model is a fine-tuned version of roberta-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6880
* Accuracy: 0.5634
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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5... |
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train
This model is a fine-tuned version of [distilbert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train", "results": []}]} | silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003_100train | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T07:54:21+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| silviacamplani/distilbert-base-uncased-finetuned-ner-conll2003\_100train
========================================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.4072
* Valid... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #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: {'inner\\_optimizer':... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartbole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | th1s1s1t/Reinforce-cartbole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-02T08:02:44+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_reg1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_reg1", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_reg1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T08:03:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_reg1
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6165
* Mse: 0.6165
* Mae: 0.6069
* R2: 0.4197
* Accuracy: 0.5007
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-generation | transformers | # Anakin Skywalker DialogGPT Model | {"tags": ["conversational"]} | Swervin7s/DialoGPT-medium-anakin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T08:11:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Anakin Skywalker DialogGPT Model | [
"# Anakin Skywalker DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Anakin Skywalker DialogGPT Model"
] |
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