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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | vebie91/dqn-SpaceInvadersNoFrameskip-v4-v1.1 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T07:47:44+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | 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="kavi12/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": ... | kavi12/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-26T07:53:47+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-generation | transformers |
## TextCortex AI - Product Description Generator - Electronics Model
This is one of our legacy models that was used for generating product descriptions for Electronic products. Because of the inference times, we trained this model on a very small version of the GPT-NEO with 125M parameters.
Due to its small size, we... | {"license": "mit"} | TextCortex/product_description_generator | null | [
"transformers",
"gpt_neo",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-26T07:55:26+00:00 | [] | [] | TAGS
#transformers #gpt_neo #text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## TextCortex AI - Product Description Generator - Electronics Model
This is one of our legacy models that was used for generating product descriptions for Electronic products. Because of the inference times, we trained this model on a very small version of the GPT-NEO with 125M parameters.
Due to its small size, we... | [
"## TextCortex AI - Product Description Generator - Electronics Model\n\nThis is one of our legacy models that was used for generating product descriptions for Electronic products. Because of the inference times, we trained this model on a very small version of the GPT-NEO with 125M parameters.\n\nDue to its small ... | [
"TAGS\n#transformers #gpt_neo #text-generation #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## TextCortex AI - Product Description Generator - Electronics Model\n\nThis is one of our legacy models that was used for generating product descriptions for Electronic products. Be... |
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="kavi12/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": ... | kavi12/q-FrozenLake-v1-8x8-noSlippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-26T08:31:46+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# convnext-tiny-finetuned-dogfood
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder", "lewtun/dog_food"], "metrics": ["accuracy"], "model-index": [{"name": "convnext-tiny-finetuned-dogfood", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "lewtun/dog_food",... | abhishek/convnext-tiny-finetuned-dogfood | null | [
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"dataset:lewtun/dog_food",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T08:36:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #convnext #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| convnext-tiny-finetuned-dogfood
===============================
This model is a fine-tuned version of facebook/convnext-tiny-224 on the lewtun/dog\_food dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9277
* Accuracy: 0.7253
Model description
-----------------
More information needed... | [
"### 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 #convnext #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used d... |
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="kavi12/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | kavi12/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-26T08:39:59+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
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"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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-18-finetuned-dogfood
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder", "lewtun/dog_food"], "metrics": ["accuracy"], "model-index": [{"name": "resnet-18-finetuned-dogfood", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "lewtun/dog_food", "ty... | douwekiela/resnet-18-finetuned-dogfood | null | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"dataset:lewtun/dog_food",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-26T08:42:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| resnet-18-finetuned-dogfood
===========================
This model is a fine-tuned version of microsoft/resnet-18 on the lewtun/dog\_food dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2991
* Accuracy: 0.896
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 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 #resnet #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters we... |
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-finetuned-dogfood
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder", "lewtun/dog_food"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-finetuned-dogfood", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "lewtun/dog_food", "ty... | sasha/swin-tiny-finetuned-dogfood | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"dataset:lewtun/dog_food",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T08:46:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-finetuned-dogfood
===========================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the lewtun/dog\_food dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1959
* Accuracy: 0.988
Model description
-----------------
More information nee... | [
"### 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 #swin #image-classification #generated_from_trainer #dataset-imagefolder #dataset-lewtun/dog_food #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
text-generation | transformers | # OPT 6B - Nerys
## Model Description
OPT 6B-Nerys is a finetune created using Facebook's OPT model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most parts of the dataset have bee... | {"language": "en", "license": "other", "commercial": false} | KoboldAI/OPT-6B-nerys-v2 | null | [
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-26T09:24:25+00:00 | [
"2205.01068"
] | [
"en"
] | TAGS
#transformers #pytorch #opt #text-generation #en #arxiv-2205.01068 #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # OPT 6B - Nerys
## Model Description
OPT 6B-Nerys is a finetune created using Facebook's OPT model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most parts of the dataset have bee... | [
"# OPT 6B - Nerys",
"## Model Description\nOPT 6B-Nerys is a finetune created using Facebook's OPT model.",
"## Training data\nThe training data contains around 2500 ebooks in various genres (the \"Pike\" dataset), a CYOA dataset called \"CYS\" and 50 Asian \"Light Novels\" (the \"Manga-v1\" dataset).\nMost par... | [
"TAGS\n#transformers #pytorch #opt #text-generation #en #arxiv-2205.01068 #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# OPT 6B - Nerys",
"## Model Description\nOPT 6B-Nerys is a finetune created using Facebook's OPT model.",
"## Training da... |
image-classification | transformers |
Thyroid nodule is one of the most common endocrine carcinomas. Due to its higher reveal ability and ability to distinguish between benign and malignant nodules in pathological features, ultrasonography has become the most widely used modality for finding and diagnosing thyroid cancer when compared to CT and MRI.
In t... | {"tags": ["medicalimaging", "thyroidtumor"], "metrics": ["accuracy"]} | SerdarHelli/ThyroidTumorClassificationModel | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"medicalimaging",
"thyroidtumor",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-26T09:52:45+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #medicalimaging #thyroidtumor #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Thyroid nodule is one of the most common endocrine carcinomas. Due to its higher reveal ability and ability to distinguish between benign and malignant nodules in pathological features, ultrasonography has become the most widely used modality for finding and diagnosing thyroid cancer when compared to CT and MRI.
In t... | [] | [
"TAGS\n#transformers #pytorch #convnext #image-classification #medicalimaging #thyroidtumor #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# shafin/distilbert-similarity-b32-3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 3 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this mode... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | shafin/distilbert-similarity-b32-3 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T10:23:53+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# shafin/distilbert-similarity-b32-3
This is a sentence-transformers model: It maps sentences & paragraphs to a 3 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installe... | [
"# shafin/distilbert-similarity-b32-3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 3 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformer... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# shafin/distilbert-similarity-b32-3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 3 dimensional dense vector space and can be used for tasks like clus... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | FreelancerFel/dqn_SpaceInvader | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T10:35:21+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | FreelancerFel/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T10:38:55+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
fill-mask | transformers |
# Pre-trained Language Model for England and Wales Court of Appeal (Criminal Division) Decisions
## Introduction
The research for understanding the bias in criminal court decisions need the support of natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining... | {"language": ["en"], "license": "apache-2.0", "tags": ["fill-mask"], "widget": [{"text": "He carefully assessed the financial position of the <mask> disclosed within its accounts, including its pension scheme liabilities."}, {"text": "Moreover, she had chosen not to give <mask> and therefore had not provided any innoce... | tsantosh7/Bailii-Roberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T11:54:46+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #arxiv-1907.11692 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Pre-trained Language Model for England and Wales Court of Appeal (Criminal Division) Decisions
## Introduction
The research for understanding the bias in criminal court decisions need the support of natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining... | [
"# Pre-trained Language Model for England and Wales Court of Appeal (Criminal Division) Decisions",
"## Introduction\n\nThe research for understanding the bias in criminal court decisions need the support of natural language processing tools. \n\nThe pre-trained language model has greatly improved the accuracy of... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #arxiv-1907.11692 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pre-trained Language Model for England and Wales Court of Appeal (Criminal Division) Decisions",
"## Introduction\n\nThe research for understanding the bias ... |
null | null | How to use ths model?
Download the pytorch_model.bin file and execute the following:
```python
import pandas as pd
import torch
import transformers
from torch.utils.data import Dataset, DataLoader
from transformers import RobertaModel, RobertaTokenizer, BertModel, BertTokenizer
device = torch.device("cuda" if torch.c... | {"license": "mit"} | sohomghosh/LIPI_FinSim4_ESG_task2 | null | [
"pytorch",
"license:mit",
"region:us"
] | null | 2022-06-26T12:02:54+00:00 | [] | [] | TAGS
#pytorch #license-mit #region-us
| How to use ths model?
Download the pytorch_model.bin file and execute the following:
Our work can be cited as follows:
| [] | [
"TAGS\n#pytorch #license-mit #region-us \n"
] |
question-answering | transformers |
The model has been trained on the second version of the [SQuAD_es](https://huggingface.co/datasets/squad_es) database. It is a question-answering dataset automatically translated from SQUAD to Spanish. This version includes the possibility that the answer does not exist within the context.
The pretrained model used i... | {"language": ["es"], "tags": ["question-answering"], "datasets": ["squad_es"], "metrics": ["f1", "em"], "model-index": [{"name": "beto-base-spanish-squades2", "results": [{"task": {"type": "question-answering", "name": "question-answering"}, "dataset": {"name": "squad_es v2.0.0", "type": "squad_es", "args": "es"}, "met... | inigopm/beto-base-spanish-squades2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"es",
"dataset:squad_es",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T12:20:24+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #question-answering #es #dataset-squad_es #model-index #endpoints_compatible #region-us
|
The model has been trained on the second version of the SQuAD_es database. It is a question-answering dataset automatically translated from SQUAD to Spanish. This version includes the possibility that the answer does not exist within the context.
The pretrained model used is "dccuchile/bert-base-spanish-wwm-cased", a... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #es #dataset-squad_es #model-index #endpoints_compatible #region-us \n"
] |
null | null | How to load the model and generate predictions?
Download the pytorch_model.bin file and execute the following:
```python
import pandas as pd
import torch
import transformers
from torch.utils.data import Dataset, DataLoader
from transformers import RobertaModel, RobertaTokenizer, BertModel, BertTokenizer
device = torc... | {"license": "mit"} | sohomghosh/finrad_model | null | [
"pytorch",
"license:mit",
"region:us"
] | null | 2022-06-26T12:34:05+00:00 | [] | [] | TAGS
#pytorch #license-mit #region-us
| How to load the model and generate predictions?
Download the pytorch_model.bin file and execute the following:
| [] | [
"TAGS\n#pytorch #license-mit #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. -->
# mdeberta-wl-base-es
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "mdeberta-wl-base-es", "results": []}]} | plncmm/mdeberta-wl-base-es | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T12:38:09+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# mdeberta-wl-base-es
This model is a fine-tuned version of microsoft/mdeberta-v3-base 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 hyperpa... | [
"# mdeberta-wl-base-es\n\nThis model is a fine-tuned version of microsoft/mdeberta-v3-base 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 proce... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# mdeberta-wl-base-es\n\nThis model is a fine-tuned version of microsoft/mdeberta-v3-base on an unknown dataset.",
"## Model description\n\nMore information need... |
question-answering | transformers |
# Finetuned legal contract review QA model based 👩⚖️ 📑
Best model presented in the master thesis [*Exploring CUAD using RoBERTa span-selection QA models for legal contract review*](https://github.com/gustavhartz/transformers-legal-tasks) for QA on the Contract Understanding Atticus Dataset. Full training logic and... | {"language": "en", "datasets": ["cuad"]} | gustavhartz/roberta-base-cuad-finetuned | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:cuad",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-26T12:51:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-cuad #endpoints_compatible #has_space #region-us
| Finetuned legal contract review QA model based ️
=================================================
Best model presented in the master thesis *Exploring CUAD using RoBERTa span-selection QA models for legal contract review* for QA on the Contract Understanding Atticus Dataset. Full training logic and associated thesi... | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #en #dataset-cuad #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# Model Card of `lmqg/mbart-large-cc25-esquad-qg`
This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.co... | {"language": "es", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_esquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.", "example_... | research-backup/mbart-large-cc25-esquad-qg | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"question generation",
"es",
"dataset:lmqg/qg_esquad",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T12:57:27+00:00 | [
"2210.03992"
] | [
"es"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #question generation #es #dataset-lmqg/qg_esquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/mbart-large-cc25-esquad-qg'
===============================================
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg\_esquad (dataset\_name: default) via 'lmqg'.
### Overview
* Language model: facebook/mbart-large-cc25
* Language... | [
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: es\n* Training data: lmqg/qg\\_esquad (default)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #question generation #es #dataset-lmqg/qg_esquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/mbart-large-cc25\n* Language: es\n* Training data:... |
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="Nikkisora/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"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": ... | Nikkisora/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-26T12:58:48+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 | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | kavi12/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T12:59:56+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Nikkisora/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-26T13:10:12+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# exper_batch_8_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-bas... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_8_e4", "results": []}]} | sudo-s/exper_batch_8_e4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T13:18:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper\_batch\_8\_e4
===================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3353
* Accuracy: 0.9183
Model description
-----------------
More information needed
Intende... | [
"### 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: 4\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T13:19:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v4
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v3 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* ... |
text2text-generation | transformers |
<!-- 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-ru-en-finetuned-ru-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ru-en-finetuned-ru-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | BukaByaka/opus-mt-ru-en-finetuned-ru-to-en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T13:26:38+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ru-en-finetuned-ru-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ru-en on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4092
* Bleu: 30.4049
* Gen Len: 26.3911
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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
feature-extraction | transformers |
Note: This model may not perfectly replicate the numbers mentioned in the paper (\cite{chopra-ghosh-2021-term}) as unlike the original one it has been trained with lower batches sizes for fewer epochs.
The source code for training will son be made available in https://github.com/sohomghosh/FinSim_Financial_Hypernym_d... | {"license": "mit"} | sohomghosh/LIPI_FinSim3_Hypernym | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-26T13:33:29+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #license-mit #endpoints_compatible #has_space #region-us
|
Note: This model may not perfectly replicate the numbers mentioned in the paper (\cite{chopra-ghosh-2021-term}) as unlike the original one it has been trained with lower batches sizes for fewer epochs.
The source code for training will son be made available in URL
Use the following code to import this in Transfor... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #license-mit #endpoints_compatible #has_space #region-us \n"
] |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | yaswanth/identify-my-cat | null | [
"fastai",
"region:us"
] | null | 2022-06-26T13:40:52+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ryanblak/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T14:00:58+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1040935781
- CO2 Emissions (in grams): 326.52733725745725
## Validation Metrics
- Loss: 1.9157543182373047
- Rouge1: 0.4843
- Rouge2: 0.0
- RougeL: 0.4843
- RougeLsum: 0.4843
- Gen Len: 10.9718
## Usage
You can use cURL to access this model... | {"language": "zh", "tags": "autotrain", "datasets": ["p123/autotrain-data-my-sum"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 326.52733725745725} | p123/autotrain-my-sum-1040935781 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"zh",
"dataset:p123/autotrain-data-my-sum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-26T14:19:08+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #zh #dataset-p123/autotrain-data-my-sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1040935781
- CO2 Emissions (in grams): 326.52733725745725
## Validation Metrics
- Loss: 1.9157543182373047
- Rouge1: 0.4843
- Rouge2: 0.0
- RougeL: 0.4843
- RougeLsum: 0.4843
- Gen Len: 10.9718
## Usage
You can use cURL to access this model... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1040935781\n- CO2 Emissions (in grams): 326.52733725745725",
"## Validation Metrics\n\n- Loss: 1.9157543182373047\n- Rouge1: 0.4843\n- Rouge2: 0.0\n- RougeL: 0.4843\n- RougeLsum: 0.4843\n- Gen Len: 10.9718",
"## Usage\n\nYou can use ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #zh #dataset-p123/autotrain-data-my-sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1040935781\n- CO2 Emis... |
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. -->
# exper_batch_8_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-bas... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_8_e8", "results": []}]} | sudo-s/exper_batch_8_e8 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T14:35:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper\_batch\_8\_e8
===================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4608
* Accuracy: 0.9052
Model description
-----------------
More information needed
Intende... | [
"### 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: 8\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Dorin/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-26T15:25:29+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ivanlau/ppo-mlppolicy-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T15:38:42+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | AdiKompella/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T15:47:46+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | shubhamsalokhe/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-26T16:50:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6421
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 impo... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | ryanblak/PPO-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T17:21:51+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\nThis is a trained model of a PPO agent playing CarRacing-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code... |
text2text-generation | transformers | **This model is part of the Gramformer library** please refer to https://github.com/PrithivirajDamodaran/Gramformer/
| {} | sanjay-m1/grammar-corrector-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-26T18:00:10+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model is part of the Gramformer library please refer to URL
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | Samiul/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T18:31:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3821
* Wer: 0.3208
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | sinhprous/dqn-SpaceInvadersNoFrameskip-v4-3 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-26T18:52:34+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# exper_batch_16_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_16_e4", "results": []}]} | sudo-s/exper_batch_16_e4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T18:53:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper\_batch\_16\_e4
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3598
* Accuracy: 0.9059
Model description
-----------------
More information needed
Inten... | [
"### 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: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
text2text-generation | transformers |
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_fr/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, the... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert2gpt2_med_v3", "results": []}]} | Chemsseddine/bert2gpt2_med_v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-26T18:56:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
| <img src="URL alt="Map of positive probabilities per country." width="200"/>
bert2gpt2\_med\_v3
==================
This model is a fine-tuned version of Chemsseddine/bert2gpt2\_med\_v2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5474
* Rouge1: 31.8871
* Rouge2: 14.4411
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
text-generation | transformers |
Base model: [gpt2-large](https://huggingface.co/gpt2-large)
Fine-tuned to generate responses on a dataset of [COVID-19 public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353).... | {"license": "mit"} | TheRensselaerIDEA/gpt2-large-covid-tweet-response | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"arxiv:2204.04353",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-26T18:56:35+00:00 | [
"2204.04353"
] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #arxiv-2204.04353 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Base model: gpt2-large
Fine-tuned to generate responses on a dataset of COVID-19 public health tweets. For more information about the dataset, task and training, see our paper. This checkpoint corresponds to the lowest validation perplexity (3.36 at 2 epochs) seen during training. See Training metrics for Tensorboard... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #arxiv-2204.04353 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \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. -->
# exper_batch_16_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_16_e8", "results": []}]} | sudo-s/exper_batch_16_e8 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T19:43:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper\_batch\_16\_e8
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3951
* Accuracy: 0.9129
Model description
-----------------
More information needed
Inten... | [
"### 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: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | Anupama/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T19:52:13+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: 0.2226
* Accuracy: 0.922
* F1: 0.9219
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers | <img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
# bert2gpt2_med_v4
This model is a fine-tuned version of [Chemsseddine/bert2gpt2_med_v3](https://hugging... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bert2gpt2_med_v4", "results": []}]} | Chemsseddine/bert2gpt2_med_v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T20:24:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| <img src="URL alt="Map of positive probabilities per country." width="200"/>
bert2gpt2\_med\_v4
==================
This model is a fine-tuned version of Chemsseddine/bert2gpt2\_med\_v3 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4780
* Rouge1: 36.7502
* Rouge2: 18.5992
... | [
"### 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\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #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: 2e-05\n* train\\_batch\\_size: 1... |
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": []}]} | Shanny/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T20:27:13+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... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | kaisuke/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T20:27:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3120
- Accuracy: 0.87
- F1: 0.8696
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3120\n- Accuracy: 0.87\n- F1: 0.8696",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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. -->
# exper_batch_32_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_32_e4", "results": []}]} | sudo-s/exper_batch_32_e4 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T21:20:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper\_batch\_32\_e4
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3909
* Accuracy: 0.9067
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
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. -->
# exper_batch_32_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-ba... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "exper_batch_32_e8", "results": []}]} | sudo-s/exper_batch_32_e8 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T21:48:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| exper\_batch\_32\_e8
====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier\_mesuem1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3520
* Accuracy: 0.9113
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-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": []}]} | neweasterns/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 | 2022-06-26T23:01:04+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: 0.5206
* Wer: 0.3388
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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | lingchensanwen/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-26T23:42:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0337
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #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: 2e-05\n* train\\_batch\\_size: 32\n* eval... |
null | null | There are two folders now:
- conformer: Conformer A3T trained with all VCTK training data.
- unseen_conformer: Conformer A3T trained by excluding some speakers during the training.
| {"license": "apache-2.0"} | richardbaihe/a3t-vctk | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-27T00:01:01+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| There are two folders now:
- conformer: Conformer A3T trained with all VCTK training data.
- unseen_conformer: Conformer A3T trained by excluding some speakers during the training.
| [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | Corianas/ppo-QbertNoFrameskip-v4_4 | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-27T00:05:46+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## U... | [
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1042335811
- CO2 Emissions (in grams): 426.15271368095927
## Validation Metrics
- Loss: 1.7748287916183472
- Rouge1: 0.536
- Rouge2: 0.0
- RougeL: 0.536
- RougeLsum: 0.536
- Gen Len: 10.9089
## Usage
You can use cURL to access this model:
... | {"language": "zh", "tags": "autotrain", "datasets": ["zyxzyx/autotrain-data-sum"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 426.15271368095927} | zyxzyx/autotrain-sum-1042335811 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"zh",
"dataset:zyxzyx/autotrain-data-sum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T00:25:28+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #zh #dataset-zyxzyx/autotrain-data-sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1042335811
- CO2 Emissions (in grams): 426.15271368095927
## Validation Metrics
- Loss: 1.7748287916183472
- Rouge1: 0.536
- Rouge2: 0.0
- RougeL: 0.536
- RougeLsum: 0.536
- Gen Len: 10.9089
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1042335811\n- CO2 Emissions (in grams): 426.15271368095927",
"## Validation Metrics\n\n- Loss: 1.7748287916183472\n- Rouge1: 0.536\n- Rouge2: 0.0\n- RougeL: 0.536\n- RougeLsum: 0.536\n- Gen Len: 10.9089",
"## Usage\n\nYou can use cUR... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #zh #dataset-zyxzyx/autotrain-data-sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1042335811\n- CO2 Emiss... |
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. -->
# tiny_focal_alpah
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation ... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_focal_alpah", "results": []}]} | kktoto/tiny_focal_alpah | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T00:31:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_focal\_alpah
==================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0492
* Precision: 0.6951
* Recall: 0.6796
* F1: 0.6873
* Accuracy: 0.9512
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\... |
audio-classification | null |
copy of https://tfhub.dev/google/yamnet/1, https://tfhub.dev/google/coral-model/yamnet/classification/coral/1 | {"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification"], "datasets": ["AudioSet"]} | thelou1s/yamnet | null | [
"tflite",
"audio-classification",
"multilingual",
"dataset:AudioSet",
"license:apache-2.0",
"region:us"
] | null | 2022-06-27T00:39:58+00:00 | [] | [
"multilingual"
] | TAGS
#tflite #audio-classification #multilingual #dataset-AudioSet #license-apache-2.0 #region-us
|
copy of URL URL | [] | [
"TAGS\n#tflite #audio-classification #multilingual #dataset-AudioSet #license-apache-2.0 #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | tjscollins/atari-dqn | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-27T00:44:58+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | 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"]} | ra-XOr/Unity-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-06-27T01:16:39+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-classification | transformers | Toxicity LD50 prediction (regression model) based on <a href = "https://tdcommons.ai/single_pred_tasks/tox/"> Acute Toxicity LD50 </a> dataset.
For now, for the purpose of prediction, download the model. In the future, an easy colab notebook will be available. | {} | Parsa/LD50-prediction | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T01:29:40+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Toxicity LD50 prediction (regression model) based on <a href = "URL Acute Toxicity LD50 </a> dataset.
For now, for the purpose of prediction, download the model. In the future, an easy colab notebook will be available. | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
Base model: [gpt2-large](https://huggingface.co/gpt2-large)
Fine-tuned to generate responses on a dataset of [Vaccine public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). ... | {"license": "mit"} | TheRensselaerIDEA/gpt2-large-vaccine-tweet-response | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"arxiv:2204.04353",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T02:03:38+00:00 | [
"2204.04353"
] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #arxiv-2204.04353 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Base model: gpt2-large
Fine-tuned to generate responses on a dataset of Vaccine public health tweets. For more information about the dataset, task and training, see our paper. This checkpoint corresponds to the lowest validation perplexity (2.82 at 2 epochs) seen during training. See Training metrics for Tensorboard ... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #arxiv-2204.04353 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jcmc/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.46 +/... | jcmc/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-27T03:21:13+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Malaya-speech_fine-tune_realcase_27_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](http... | {"tags": ["generated_from_trainer"], "datasets": ["uob_singlish"], "model-index": [{"name": "Malaya-speech_fine-tune_realcase_27_Jun", "results": []}]} | RuiqianLi/Malaya-speech_fine-tune_realcase_27_Jun | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T04:21:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us
| Malaya-speech\_fine-tune\_realcase\_27\_Jun
===========================================
This model is a fine-tuned version of malay-huggingface/wav2vec2-xls-r-300m-mixed on the uob\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9159
* Wer: 0.3819
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size:... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-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": []}]} | dasolj/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 | 2022-06-27T04:22:57+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: 0.5501
* Wer: 0.3424
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... |
text-generation | transformers | gpt 2 puisi | {} | samroni/gpt-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T04:30:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt 2 puisi | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gopalkalpande/t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "gopalkalpande/t5-small-finetuned-xsum", "results": []}]} | gopalkalpande/t5-small-finetuned-xsum | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T05:26:01+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gopalkalpande/t5-small-finetuned-xsum
=====================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0422
* Validation Loss: 0.4407
* Train Rouge1: 19.5311
* Train Rouge2: 14.2402
* Train Rougel: 17... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'... |
null | null |
See https://github.com/k2-fsa/icefall/pull/380 | {"license": "apache-2.0"} | pkufool/icefall_librispeech_streaming_pruned_transducer_stateless_20220625 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-27T05:26:35+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
|
See URL | [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# token_final_tunned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "token_final_tunned", "results": []}]} | vinayak361/token_final_tunned | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T05:33:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| token\_final\_tunned
====================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4670
* Precision: 0.8269
* Recall: 0.8442
* F1: 0.8355
* Accuracy: 0.8516
Model description
-----------------
More infor... | [
"### 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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bigbird-base-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/bigbird-base-finetuned-big_patent](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["big_patent"], "model-index": [{"name": "bigbird-base-finetuned-big_patent", "results": []}]} | robingeibel/bigbird-base-finetuned-big_patent | null | [
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"fill-mask",
"generated_from_trainer",
"dataset:big_patent",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T06:03:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #big_bird #fill-mask #generated_from_trainer #dataset-big_patent #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bigbird-base-finetuned-big\_patent
==================================
This model is a fine-tuned version of robingeibel/bigbird-base-finetuned-big\_patent on the big\_patent dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0686
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #big_bird #fill-mask #generated_from_trainer #dataset-big_patent #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-academic
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["elsevier-oa-cc-by"], "model-index": [{"name": "roberta-base-finetuned-academic", "results": []}]} | egumasa/roberta-base-finetuned-academic | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:elsevier-oa-cc-by",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T06:06:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-academic
===============================
This model is a fine-tuned version of roberta-base on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1158
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: 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: 5\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-nsc-final_1-google-colab
This model was trained from scratch on the None dataset.
## Model description
More informati... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-nsc-final_1-google-colab", "results": []}]} | YuanWellspring/wav2vec2-nsc-final_1-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T06:57:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-nsc-final_1-google-colab
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
"# wav2vec2-nsc-final_1-google-colab\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec2-nsc-final_1-google-colab\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended us... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reproduce-unsup-roberta-base-avg
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "reproduce-unsup-roberta-base-avg", "results": []}]} | JeremiahZ/reproduce-unsup-roberta-base-avg | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"generated_from_trainer",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T07:09:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #generated_from_trainer #en #license-mit #endpoints_compatible #region-us
|
# reproduce-unsup-roberta-base-avg
This model is a fine-tuned version of roberta-base 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 hyperpar... | [
"# reproduce-unsup-roberta-base-avg\n\nThis model is a fine-tuned version of roberta-base 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 proced... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #generated_from_trainer #en #license-mit #endpoints_compatible #region-us \n",
"# reproduce-unsup-roberta-base-avg\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-fine-tuned-blbooksgenre
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["blbooksgenre"], "model-index": [{"name": "distilbert-base-cased-fine-tuned-blbooksgenre", "results": []}]} | TheBritishLibrary/distilbert-base-cased-fine-tuned-blbooksgenre | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:blbooksgenre",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T07:18:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-blbooksgenre #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-cased-fine-tuned-blbooksgenre
=============================================
This model is a fine-tuned version of distilbert-base-cased on the blbooksgenre dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9631
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-blbooksgenre #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\... |
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. -->
# Indobert-QA-finetuned-squad
This model is a fine-tuned version of [Rifky/Indobert-QA](https://huggingface.co/Rifky/Indobert-QA) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Indobert-QA-finetuned-squad", "results": []}]} | botika/Indobert-QA-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T07:19:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Indobert-QA-finetuned-squad
===========================
This model is a fine-tuned version of Rifky/Indobert-QA on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 15.2477
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Corianas/ppo-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-27T07:22:35+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Corianas/ppo-SpaceInvadersNoFrameskip-v4.loadbest | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-27T07:24:26+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reproduce-unsup-bert-base-uncased-avg
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "reproduce-unsup-bert-base-uncased-avg", "results": []}]} | JeremiahZ/reproduce-unsup-bert-base-uncased-avg | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"en",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T07:34:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
|
# reproduce-unsup-bert-base-uncased-avg
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# reproduce-unsup-bert-base-uncased-avg\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"# reproduce-unsup-bert-base-uncased-avg\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reproduce-sup-roberta-base-avg
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an u... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "reproduce-sup-roberta-base-avg", "results": []}]} | JeremiahZ/reproduce-sup-roberta-base-avg | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"generated_from_trainer",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T07:38:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #generated_from_trainer #en #license-mit #endpoints_compatible #region-us
|
# reproduce-sup-roberta-base-avg
This model is a fine-tuned version of roberta-base 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 hyperparam... | [
"# reproduce-sup-roberta-base-avg\n\nThis model is a fine-tuned version of roberta-base 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 procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #generated_from_trainer #en #license-mit #endpoints_compatible #region-us \n",
"# reproduce-sup-roberta-base-avg\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended us... |
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. -->
# dbgbert-finetuned-squad
This model was trained from scratch on the squad dataset.
## Model description
More information needed... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "dbgbert-finetuned-squad", "results": []}]} | Shanny/dbgbert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T08:04:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# dbgbert-finetuned-squad
This model was trained from scratch on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hy... | [
"# dbgbert-finetuned-squad\n\nThis model was trained from scratch 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",
"### Training hyp... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# dbgbert-finetuned-squad\n\nThis model was trained from scratch on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & lim... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Laure996/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T08:31:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0663
* Precision: 0.9329
* Recall: 0.9478
* F1: 0.9403
* Accuracy: 0.9855
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522
## Validation Metrics
- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103
## Usage
You c... | {"language": "en", "tags": "autotrain", "datasets": ["danielmantisnlp/autotrain-data-oms-ner-bi"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.425282392185522} | danielmantisnlp/autotrain-oms-ner-bi-1044135953 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:danielmantisnlp/autotrain-data-oms-ner-bi",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T08:38:38+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-danielmantisnlp/autotrain-data-oms-ner-bi #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1044135953
- CO2 Emissions (in grams): 1.425282392185522
## Validation Metrics
- Loss: 0.4587894678115845
- Accuracy: 0.8957797220792589
- Precision: 0.553921568627451
- Recall: 0.6793587174348698
- F1: 0.6102610261026103
## Usage
You c... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1044135953\n- CO2 Emissions (in grams): 1.425282392185522",
"## Validation Metrics\n\n- Loss: 0.4587894678115845\n- Accuracy: 0.8957797220792589\n- Precision: 0.553921568627451\n- Recall: 0.6793587174348698\n- F1: 0.610261026102610... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-danielmantisnlp/autotrain-data-oms-ner-bi #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1044135953\n- CO2 Emissions (... |
translation | transformers | ### en-de
* source group: English
* target group: German
* OPUS readme: [eng-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-deu/README.md)
* model: transformer-big
* source language(s): eng
* target language(s): deu
* raw source language(s): eng
* raw target language(s): deu
* model... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation"]} | Rahulrr/language_model_en_de | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T09:09:17+00:00 | [] | [
"en",
"de"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### en-de
* source group: English
* target group: German
* OPUS readme: eng-deu
* model: transformer-big
* source language(s): eng
* target language(s): deu
* raw source language(s): eng
* raw target language(s): deu
* model: transformer-big
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download o... | [
"### en-de\n\n\n* source group: English\n* target group: German\n* OPUS readme: eng-deu\n* model: transformer-big\n* source language(s): eng\n* target language(s): deu\n* raw source language(s): eng\n* raw target language(s): deu\n* model: transformer-big\n* pre-processing: normalization + SentencePiece (spm32k,spm... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### en-de\n\n\n* source group: English\n* target group: German\n* OPUS readme: eng-deu\n* model: transformer-big\n* source language(s): eng\n* target la... |
fill-mask | transformers |
# Erlangshen-Ubert-110M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
采用统一的框架处理多种抽取任务，AIWIN2022的冠军方案，1.1亿参数量的中文UBERT-Base。
Adopting a unified framework to handle multiple information extraction tas... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "NLU", "Sentiment", "Chinese"], "inference": false, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d"}]} | IDEA-CCNL/Erlangshen-Ubert-110M-Chinese | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"NLU",
"Sentiment",
"Chinese",
"zh",
"arxiv:2206.12094",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-06-27T10:01:04+00:00 | [
"2206.12094",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #NLU #Sentiment #Chinese #zh #arxiv-2206.12094 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #has_space #region-us
| Erlangshen-Ubert-110M-Chinese
=============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
采用统一的框架处理多种抽取任务，AIWIN2022的冠军方案，1.1亿参数量的中文UBERT-Base。
Adopting a unified framework to handle multiple information extraction tasks, AIWIN2022's champion solut... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #NLU #Sentiment #Chinese #zh #arxiv-2206.12094 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Erlangshen-Ubert-330M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
采用统一的框架处理多种抽取任务，AIWIN2022的冠军方案，3.3亿参数量的中文UBERT-Large。
Adopting a unified framework to handle multiple information extraction ta... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "NLU", "Sentiment", "Chinese"], "inference": false} | IDEA-CCNL/Erlangshen-Ubert-330M-Chinese | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"NLU",
"Sentiment",
"Chinese",
"zh",
"arxiv:2206.12094",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-06-27T10:01:33+00:00 | [
"2206.12094",
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #NLU #Sentiment #Chinese #zh #arxiv-2206.12094 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #region-us
| Erlangshen-Ubert-330M-Chinese
=============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
采用统一的框架处理多种抽取任务，AIWIN2022的冠军方案，3.3亿参数量的中文UBERT-Large。
Adopting a unified framework to handle multiple information extraction tasks, AIWIN2022's champion solu... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #NLU #Sentiment #Chinese #zh #arxiv-2206.12094 #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="zezafa/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": ... | zezafa/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-27T10:47:03+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="zezafa/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": "7.38 +/... | zezafa/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-27T10:52:09+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | sentence-transformers |
# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. | {"language": "multilingual", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-embeddings"} | Genario/multilingual_paraphrase | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"sentence-embeddings",
"multilingual",
"license:apache-2.0",
"region:us"
] | null | 2022-06-27T11:17:36+00:00 | [] | [
"multilingual"
] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #sentence-embeddings #multilingual #license-apache-2.0 #region-us
|
# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. | [
"# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
] | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #sentence-embeddings #multilingual #license-apache-2.0 #region-us \n",
"# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to ... |
text2text-generation | transformers |
# Querido Diario Autoencoder
Autoencoder based on portuguese BERT using the Querido Diario dataset | {"language": ["pt"], "datasets": ["jvanz/querido_diario"]} | jvanz/querido_diario_autoencoder | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"pt",
"dataset:jvanz/querido_diario",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T11:48:50+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #pt #dataset-jvanz/querido_diario #autotrain_compatible #endpoints_compatible #region-us
|
# Querido Diario Autoencoder
Autoencoder based on portuguese BERT using the Querido Diario dataset | [
"# Querido Diario Autoencoder\n\nAutoencoder based on portuguese BERT using the Querido Diario dataset"
] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #pt #dataset-jvanz/querido_diario #autotrain_compatible #endpoints_compatible #region-us \n",
"# Querido Diario Autoencoder\n\nAutoencoder based on portuguese BERT using the Querido Diario dataset"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gopalkalpande/t5-small-finetuned-bbc-news-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-sma... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "gopalkalpande/t5-small-finetuned-bbc-news-summarization", "results": []}]} | gopalkalpande/t5-small-finetuned-bbc-news-summarization | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T12:12:49+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gopalkalpande/t5-small-finetuned-bbc-news-summarization
=======================================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7637
* Validation Loss: 0.3528
* Train Rouge1: 19.4783
* Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 4e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.001}\n* training\\_precision: float32",... | [
"TAGS\n#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'... |
image-classification | transformers |
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repositor... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | Matthijs/mobilenet_v2_1.0_224 | null | [
"transformers",
"pytorch",
"coreml",
"mobilenet_v2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1801.04381",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T12:30:29+00:00 | [
"1801.04381"
] | [] | TAGS
#transformers #pytorch #coreml #mobilenet_v2 #image-classification #vision #dataset-imagenet-1k #arxiv-1801.04381 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository.
Disclaimer: The team releasing Mo... | [
"# MobileNet V2\n\nMobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository.\n\nDisclaimer: The team rele... | [
"TAGS\n#transformers #pytorch #coreml #mobilenet_v2 #image-classification #vision #dataset-imagenet-1k #arxiv-1801.04381 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# MobileNet V2\n\nMobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNet... |
image-classification | transformers |
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repositor... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | Matthijs/mobilenet_v2_1.4_224 | null | [
"transformers",
"pytorch",
"coreml",
"mobilenet_v2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1801.04381",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T12:32:28+00:00 | [
"1801.04381"
] | [] | TAGS
#transformers #pytorch #coreml #mobilenet_v2 #image-classification #vision #dataset-imagenet-1k #arxiv-1801.04381 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository.
Disclaimer: The team releasing Mo... | [
"# MobileNet V2\n\nMobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in this repository.\n\nDisclaimer: The team rele... | [
"TAGS\n#transformers #pytorch #coreml #mobilenet_v2 #image-classification #vision #dataset-imagenet-1k #arxiv-1801.04381 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# MobileNet V2\n\nMobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNet... |
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-wl-base-es
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wl-base-es", "results": []}]} | plncmm/gpt2-wl-base-es | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T12:57:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-wl-base-es
This model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne 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 hyperparam... | [
"# gpt2-wl-base-es\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne 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 procedur... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-wl-base-es\n\nThis model is a fine-tuned version of PlanTL-GOB-ES/gpt2-base-bne on an unknown dataset.",
"## Model descr... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v5
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v5", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v5 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T13:51:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v5
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v4 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xlsum-chinese-tradition
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xlsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xlsum-chinese-tradition", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xlsum", "type": ... | elliotthwang/t5-small-finetuned-xlsum-chinese-tradition | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T14:05:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xlsum-chinese-tradition
==========================================
This model is a fine-tuned version of t5-small on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2061
* Rouge1: 0.8887
* Rouge2: 0.0671
* Rougel: 0.889
* Rougelsum: 0.8838
* Gen Len: 6.8779
... | [
"### 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: 6\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | OptimalHoiboy/DialoGPT-small-kasumai | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-27T14:16:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | jcastanyo/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-27T14:42:39+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
automatic-speech-recognition | 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-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | nawta/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T15:04:16+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-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: 0.0403
* Wer: 0.0168
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\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\\_step... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_b... |
summarization | transformers | # long-t5-tglobal-base-16384 + BookSum
<a href="https://colab.research.google.com/gist/pszemraj/d9a0495861776168fd5cdcd7731bc4ee/example-long-t5-tglobal-base-16384-book-summary.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Summarize long text and get a Spark... | {"license": ["apache-2.0", "bsd-3-clause"], "tags": ["summarization", "summary", "booksum", "long-document", "long-form"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the str... | pszemraj/long-t5-tglobal-base-16384-book-summary | null | [
"transformers",
"pytorch",
"rust",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"arxiv:2112.07916",
"arxiv:2105.08209",
"doi:10.57967/hf/0100",
"license:apache-2.0",
"license:... | null | 2022-06-27T15:37:26+00:00 | [
"2112.07916",
"2105.08209"
] | [] | TAGS
#transformers #pytorch #rust #onnx #safetensors #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #arxiv-2112.07916 #arxiv-2105.08209 #doi-10.57967/hf/0100 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatib... | # long-t5-tglobal-base-16384 + BookSum
<a href="URL
<img src="URL alt="Open In Colab"/>
</a>
Summarize long text and get a SparkNotes-esque summary of arbitrary topics!
- generalizes reasonably well to academic & narrative text.
- A simple example/use case on ASR is here.
- Example notebook in Colab (_click... | [
"# long-t5-tglobal-base-16384 + BookSum\n\n <a href=\"URL\n <img src=\"URL alt=\"Open In Colab\"/>\n</a>\n\nSummarize long text and get a SparkNotes-esque summary of arbitrary topics!\n\n- generalizes reasonably well to academic & narrative text.\n- A simple example/use case on ASR is here.\n- Example notebo... | [
"TAGS\n#transformers #pytorch #rust #onnx #safetensors #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #arxiv-2112.07916 #arxiv-2105.08209 #doi-10.57967/hf/0100 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_co... |
null | transformers |
# Model description
An XLM-RoBERTa reading comprehension model for [TyDiQA Primary Tasks](https://arxiv.org/abs/2003.05002).
The model is initialized with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large/) and fine-tuned on the [TyDiQA train data](https://huggingface.co/datasets/tydiqa).
## Intended us... | {"language": ["multilingual"], "tags": ["MRC", "TyDiQA", "xlm-roberta-large"]} | PrimeQA/tydiqa-primary-task-xlm-roberta-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"MRC",
"TyDiQA",
"xlm-roberta-large",
"multilingual",
"arxiv:2003.05002",
"arxiv:1911.02116",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T16:19:37+00:00 | [
"2003.05002",
"1911.02116"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #MRC #TyDiQA #xlm-roberta-large #multilingual #arxiv-2003.05002 #arxiv-1911.02116 #endpoints_compatible #region-us
|
# Model description
An XLM-RoBERTa reading comprehension model for TyDiQA Primary Tasks.
The model is initialized with xlm-roberta-large and fine-tuned on the TyDiQA train data.
## Intended uses & limitations
You can use the raw model for the reading comprehension task. Biases associated with the pre-existing lan... | [
"# Model description\n\nAn XLM-RoBERTa reading comprehension model for TyDiQA Primary Tasks.\n\nThe model is initialized with xlm-roberta-large and fine-tuned on the TyDiQA train data.",
"## Intended uses & limitations\n\nYou can use the raw model for the reading comprehension task. Biases associated with the pr... | [
"TAGS\n#transformers #pytorch #xlm-roberta #MRC #TyDiQA #xlm-roberta-large #multilingual #arxiv-2003.05002 #arxiv-1911.02116 #endpoints_compatible #region-us \n",
"# Model description\n\nAn XLM-RoBERTa reading comprehension model for TyDiQA Primary Tasks.\n\nThe model is initialized with xlm-roberta-large and fi... |
null | transformers |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-... | {"language": ["en"], "license": ["mit"], "tags": ["BERT", "MNLI", "NLI", "transformer", "pre-training"]} | drhyrum/bert-tiny-torch-vuln | null | [
"transformers",
"pytorch",
"bert",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T16:26:26+00:00 | [
"1908.08962",
"2110.01518"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #region-us
|
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository.
This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium. They were introduced in the study 'Well-Read Students Learn Better:... | [] | [
"TAGS\n#transformers #pytorch #bert #BERT #MNLI #NLI #transformer #pre-training #en #arxiv-1908.08962 #arxiv-2110.01518 #license-mit #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | Eleven/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T16:59:32+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-emotion
=========================================
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: 0.2263
* Accuracy: 0.9225
* F1: 0.9221
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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... |
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