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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["precision", "recall"], "model-index": [{"name": "bert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emo... | tamarab/bert-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T15:45:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-emotion
============
This model is a fine-tuned version of distilbert-base-cased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1347
* Precision: 0.7463
* Recall: 0.7096
* Fscore: 0.7209
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text2text-generation | transformers |
## Model in Action 🚀
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_pretrained('valurank/t5-paraphraser')... | {"language": "en", "license": "other"} | valurank/t5-paraphraser | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T15:47:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## Model in Action
## Output
| [
"## Model in Action",
"## Output"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Model in Action",
"## Output"
] |
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | miyagawaorj/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T15:51:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2454
* Accuracy: 0.9474
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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
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. -->
# scibert_scivocab_cased-new-finetuned-leukaemia
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://hu... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "scibert_scivocab_cased-new-finetuned-leukaemia", "results": []}]} | ENM/scibert_scivocab_cased-new-finetuned-leukaemia | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T15:52:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| scibert\_scivocab\_cased-new-finetuned-leukaemia
================================================
This model is a fine-tuned version of allenai/scibert\_scivocab\_cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9055
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-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: 8\n* eval\\_batch... |
text2text-generation | transformers |
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are f... | {"language": "en", "license": "apache-2.0", "datasets": ["Super-NaturalInstructions"]} | allenai/tk-instruct-large-def-pos | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:Super-NaturalInstructions",
"arxiv:1910.10683",
"arxiv:2204.07705",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T16:02:10+00:00 | [
"1910.10683",
"2204.07705"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-Super-NaturalInstructions #arxiv-1910.10683 #arxiv-2204.07705 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained T5 models, they are fine-tuned on a large number of tasks... | [
"# Model description\n\nTk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained T5 models, they are fine-tuned on a large number of... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-Super-NaturalInstructions #arxiv-1910.10683 #arxiv-2204.07705 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model description\n\nTk-Instruct is a series of encoder-decode... |
text2text-generation | transformers |
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are f... | {"language": "en", "license": "apache-2.0", "datasets": ["Super-NaturalInstructions"]} | allenai/tk-instruct-base-def-pos | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:Super-NaturalInstructions",
"arxiv:1910.10683",
"arxiv:2204.07705",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T16:10:40+00:00 | [
"1910.10683",
"2204.07705"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-Super-NaturalInstructions #arxiv-1910.10683 #arxiv-2204.07705 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained T5 models, they are fine-tuned on a large number of tasks... | [
"# Model description\n\nTk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained T5 models, they are fine-tuned on a large number of... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-Super-NaturalInstructions #arxiv-1910.10683 #arxiv-2204.07705 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model description\n\nTk-Instruct is a series of encoder-decode... |
text2text-generation | transformers |
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are f... | {"language": "en", "license": "apache-2.0", "datasets": ["Super-NaturalInstructions"]} | allenai/tk-instruct-small-def-pos | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:Super-NaturalInstructions",
"arxiv:1910.10683",
"arxiv:2204.07705",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T16:15:03+00:00 | [
"1910.10683",
"2204.07705"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-Super-NaturalInstructions #arxiv-1910.10683 #arxiv-2204.07705 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained T5 models, they are fine-tuned on a large number of tasks... | [
"# Model description\n\nTk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained T5 models, they are fine-tuned on a large number of... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-Super-NaturalInstructions #arxiv-1910.10683 #arxiv-2204.07705 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Model description\n\nTk-Instruct is a series of encoder-decode... |
text2text-generation | transformers |
Work by [Frederico Vicente](https://huggingface.co/mrvicente) & [Diogo Tavares](https://huggingface.co/d-c-t). We finetuned BART Large for the task of generative question answering. It was trained on eli5, askScience and stackexchange using the following forums: pets, cooking, gardening, diy, crafts.
Check demo: htt... | {"language": "en", "tags": ["generative qa"], "datasets": ["eli5", "stackexchange(pets, cooking, gardening, diy, crafts)"]} | MrVicente/bart_qa_assistant | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generative qa",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T16:36:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #generative qa #en #autotrain_compatible #endpoints_compatible #region-us
|
Work by Frederico Vicente & Diogo Tavares. We finetuned BART Large for the task of generative question answering. It was trained on eli5, askScience and stackexchange using the following forums: pets, cooking, gardening, diy, crafts.
Check demo: URL
### Usage
Have fun! | [
"### Usage\n\n\n\nHave fun!"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generative qa #en #autotrain_compatible #endpoints_compatible #region-us \n",
"### Usage\n\n\n\nHave fun!"
] |
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... | Photons/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-20T16:57:42+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... | Battu007/V3_PPO_LunarLander_v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-20T17:05:31+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... |
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. -->
# albert-xl-v2-finetuned-squad
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-xl-v2-finetuned-squad", "results": []}]} | anas-awadalla/albert-xl-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T17:16:26+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# albert-xl-v2-finetuned-squad
This model is a fine-tuned version of albert-xlarge-v2 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 hyperpara... | [
"# albert-xl-v2-finetuned-squad\n\nThis model is a fine-tuned version of albert-xlarge-v2 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 procedu... | [
"TAGS\n#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# albert-xl-v2-finetuned-squad\n\nThis model is a fine-tuned version of albert-xlarge-v2 on the squad dataset.",
"## Model description\n\nMore information n... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **MountainCar-v0**
This is a trained model of a **TRPO** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mount... | sb3/trpo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-20T17:20:31+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing MountainCar-v0
This is a trained model of a TRPO agent playing MountainCar-v0
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (wi... | [
"# TRPO Agent playing MountainCar-v0\nThis is a trained model of a TRPO agent playing MountainCar-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
... | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing MountainCar-v0\nThis is a trained model of a TRPO agent playing MountainCar-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framewo... |
sentence-similarity | sentence-transformers |
# Ukhushn/ukhushn
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy wh... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Ukhushn/ukhushn | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T18:28:21+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Ukhushn/ukhushn
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 installed:
Then you ca... | [
"# Ukhushn/ukhushn\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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-transformers installed:\n\n\... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Ukhushn/ukhushn\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... |
sentence-similarity | sentence-transformers |
# Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Trans... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T19:07:06+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 sente... | [
"# Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 ... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector sp... |
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="dmitry-np/q-FrozenLake-v1-4x4-non-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-non-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type"... | dmitry-np/q-FrozenLake-v1-4x4-non-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-20T19:26:38+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="dmitry-np/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | dmitry-np/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-20T19:32:09+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"
] |
text-generation | transformers |
# South Park Dialog | {"tags": ["conversational"]} | rongina/DialoGPT-small-cartman | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T19:36:03+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# South Park Dialog | [
"# South Park Dialog"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# South Park Dialog"
] |
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="dmitry-np/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "met... | dmitry-np/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-20T19:50:33+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text2text-generation | transformers | hello
| {} | remotejob/bert2bertv4_v3 | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T20:09:31+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| hello
| [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # troska DialogGPT models
| {"tags": ["conversational"]} | fransoa/arrombado-dms | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T20:36:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # troska DialogGPT models
| [
"# troska DialogGPT models"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# troska DialogGPT models"
] |
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="btsas/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribut... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "F... | btsas/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-20T20:44:47+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"
] |
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="btsas/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
en... | {"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 +/... | btsas/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-20T20:47:30+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
fill-mask | transformers | # roberta-tiny
Tiny untrained model for testing purposes | {} | arampacha/roberta-tiny | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-20T20:57:19+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # roberta-tiny
Tiny untrained model for testing purposes | [
"# roberta-tiny\n\nTiny untrained model for testing purposes"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# roberta-tiny\n\nTiny untrained model for testing purposes"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1455287025821790214/c0-K... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/slayersiu/1653488944264/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/slayersiu | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T21:27:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
DR. RASMUS
@slayersiu
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **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="fmcurti/q-FrozenLake-v1-8x8-non-slippery", 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-v1-8x8-non-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type"... | fmcurti/q-FrozenLake-v1-8x8-non-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-20T22:14:19+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"
] |
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. -->
# albert-xxl-v2-finetuned-squad
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-xxl-v2-finetuned-squad", "results": []}]} | anas-awadalla/albert-xxl-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T22:34:11+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# albert-xxl-v2-finetuned-squad
This model is a fine-tuned version of albert-xxlarge-v2 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 hyperpa... | [
"# albert-xxl-v2-finetuned-squad\n\nThis model is a fine-tuned version of albert-xxlarge-v2 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 proce... | [
"TAGS\n#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# albert-xxl-v2-finetuned-squad\n\nThis model is a fine-tuned version of albert-xxlarge-v2 on the squad dataset.",
"## Model description\n\nMore information... |
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-medium-final
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-medium-final", "results": []}]} | Dizzykong/gpt2-medium-final | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T22:45:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-medium-final
This model is a fine-tuned version of gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fol... | [
"# gpt2-medium-final\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-medium-final\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore inform... |
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. -->
# deep-pavlov-framebank-hidesize
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepP... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deep-pavlov-framebank-hidesize", "results": []}]} | ruselkomp/deep-pavlov-framebank-hidesize | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-20T22:58:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| deep-pavlov-framebank-hidesize
==============================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0985
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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\\_batch\\_size: 16\n* see... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1566448713224142849/MA1q... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/mrquinnzard/1662397648914/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/mrquinnzard | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T23:18:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
MrQuinnzard X
@mrquinnzard
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1520965807374835712/oz5X... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/darcywubot/1653092857463/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/darcywubot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T23:27:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Darcy Bot
@darcywubot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | 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-medium-recipes
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown datase... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-medium-recipes", "results": []}]} | Dizzykong/gpt2-medium-recipes | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T23:47:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-medium-recipes
This model is a fine-tuned version of gpt2-medium on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# gpt2-medium-recipes\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-medium-recipes\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.",
"## Model description\n\nMore info... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1526210961031548935/59jb... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/annebottz/1653094143094/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/annebottz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-20T23:48:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Anne Bot
@annebottz
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- 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-apala-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-apala-finetuned-emotion", "results": []}]} | apthakur/distilbert-base-uncased-apala-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T00:29:42+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-apala-finetuned-emotion
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3696
* Accuracy: 0.476
* F1: 0.4250
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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... |
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. -->
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected f... | {"language": ["lb"], "license": "mit", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-14h | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"lb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T05:01:54+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us
|
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected from URL. Then the model was fine-tuned on 14h of labelled
Luxembourgish speech from the same domain.
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model was fine-tuned on 14h of labelled\nLuxembourgish speech from the same domain.",
"## Intended uses & limitations\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us \n",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech\ncollected from ... |
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. -->
# deep-pavlov-framebank-hidesize-1
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/Dee... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "deep-pavlov-framebank-hidesize-1", "results": []}]} | ruselkomp/deep-pavlov-framebank-hidesize-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T07:04:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| deep-pavlov-framebank-hidesize-1
================================
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0967
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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\\_batch\\_size: 16\n* see... |
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. -->
# vi-adapter-32
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "vi-adapter-32", "results": []}]} | subhasisj/vi-adapter-32 | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T07:14:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us
| vi-adapter-32
=============
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.4211
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32... |
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-mini-finetuned-squad
This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-mini-finetuned-squad", "results": []}]} | anas-awadalla/bert-mini-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T07:15:50+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# bert-mini-finetuned-squad
This model is a fine-tuned version of prajjwal1/bert-mini 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 hyperpara... | [
"# bert-mini-finetuned-squad\n\nThis model is a fine-tuned version of prajjwal1/bert-mini 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 procedu... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# bert-mini-finetuned-squad\n\nThis model is a fine-tuned version of prajjwal1/bert-mini on the squad dataset.",
"## Model description\n\nMore information needed",
... |
text-classification | transformers |
# English Hotel Review MultiLabel Classification
A model trained on English Hotel Reviews from Switzerland. The base model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained ... | {"language": "eng", "license": "apache-2.0", "tags": ["bert"], "widget": [{"text": "The hotel is very nicely located", "example_title": "Example 1"}, {"text": "The reception staff were extremely helpful and very welcoming", "example_title": "Example 2"}, {"text": "There is no balcony in the rooms on the mountain side",... | Tobias/bert-base-uncased_English_MultiLable_classification | null | [
"transformers",
"tf",
"bert",
"text-classification",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T07:58:31+00:00 | [] | [
"eng"
] | TAGS
#transformers #tf #bert #text-classification #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| English Hotel Review MultiLabel Classification
==============================================
A model trained on English Hotel Reviews from Switzerland. The base model is the bert-base-uncased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then traine... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | amareelez/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T08:01:03+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... | amareelez/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T08:05:19+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... |
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. -->
#
## Model description
We pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech
collected from [RTL.lu... | {"language": ["lb"], "license": "mit", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | Lemswasabi/wav2vec2-base-luxembourgish-4h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"lb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T08:06:51+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us
|
#
## Model description
We pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech
collected from URL. Then the model was fine-tuned on 4h of labelled
Luxembourgish Speech from the same domain.
## Intended uses & limitations
More information needed
## Training and evaluation data
More i... | [
"#",
"## Model description\n\nWe pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model was fine-tuned on 4h of labelled\nLuxembourgish Speech from the same domain.",
"## Intended uses & limitations\n\nMore information needed",
"## Training and eval... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us \n",
"#",
"## Model description\n\nWe pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model w... |
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="linker81/QLearning-FrozenLake-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "QLearning-FrozenLake-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "Frozen... | linker81/QLearning-FrozenLake-v1 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T08:09:00+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
## Model description
We fine-tuned a wav2vec 2.0 base checkpoint pre-trained on LibriSpeech with 842h of unlabelled Luxembourg... | {"language": ["lb"], "license": "mit", "tags": ["automatic-speech-recognition", "generated_from_trainer"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | Lemswasabi/wav2vec2-base-librispeech-LS960h-LB842h-luxembourgish-4h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"lb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T08:09:07+00:00 | [] | [
"lb"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us
|
#
## Model description
We fine-tuned a wav2vec 2.0 base checkpoint pre-trained on LibriSpeech with 842h of unlabelled Luxembourgish speech
collected from URL. Then the model was fine-tuned on 4h of labelled
Luxembourgish Speech from the same domain.
## Intended uses & limitations
More information needed
## Tra... | [
"#",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 base checkpoint pre-trained on LibriSpeech with 842h of unlabelled Luxembourgish speech\ncollected from URL. Then the model was fine-tuned on 4h of labelled\nLuxembourgish Speech from the same domain.",
"## Intended uses & limitations\n\nMore information... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #lb #license-mit #model-index #endpoints_compatible #region-us \n",
"#",
"## Model description\n\nWe fine-tuned a wav2vec 2.0 base checkpoint pre-trained on LibriSpeech with 842h of unlabelled Luxembourgish speech\ncol... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1-4x4-no-slippery**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-no-slippery** .
## Usage
```python
model = load_from_hub(repo_id="linker81/q-learning-FrozenLake-v1-4x4-no-slippery", filename="q-learning.pkl")
# Don't forget ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-learning-FrozenLake-v1-4x4-no-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery"... | linker81/q-learning-FrozenLake-v1-4x4-no-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T08:15:44+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1-4x4-no-slippery
This is a trained model of a Q-Learning agent playing FrozenLake-v1-4x4-no-slippery .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1-4x4-no-slippery\n This is a trained model of a Q-Learning agent playing FrozenLake-v1-4x4-no-slippery .\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-v1-4x4-no-slippery\n This is a trained model of a Q-Learning agent playing FrozenLake-v1-4x4-no-slippery .\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="linker81/q-learning-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=Fal... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-learning-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value":... | linker81/q-learning-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T08:20:18+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1-4x4-slippery**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-slippery** .
## Usage
```python
model = load_from_hub(repo_id="linker81/q-learning-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check ... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-learning-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x... | linker81/q-learning-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T08:31:16+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1-4x4-slippery
This is a trained model of a Q-Learning agent playing FrozenLake-v1-4x4-slippery .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1-4x4-slippery\n This is a trained model of a Q-Learning agent playing FrozenLake-v1-4x4-slippery .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1-4x4-slippery\n This is a trained model of a Q-Learning agent playing FrozenLake-v1-4x4-slippery .\n \n ## Usage"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-data
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robe... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-data", "results": []}]} | moghis/xlm-roberta-base-finetuned-panx-de-data | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T09:44:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-data
=======================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1 Score: 0.8621
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
null | null | Riverdayspa™ is one of the Top Luxury Massage Center in Chennai. We offer Quality massage therapy all over the bustling city of Chennai.
https://www.riverdayspa.com/ | {} | Riverdayspa/bodymassagechennai | null | [
"region:us"
] | null | 2022-05-21T09:53:01+00:00 | [] | [] | TAGS
#region-us
| Riverdayspa™ is one of the Top Luxury Massage Center in Chennai. We offer Quality massage therapy all over the bustling city of Chennai.
URL | [] | [
"TAGS\n#region-us \n"
] |
feature-extraction | sentence-transformers |
# sentence-transformers/msmarco-distilbert-base-v4
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | questgen/msmarco-distilbert-base-v4-feature-extraction-pipeline | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-21T10:11:17+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/msmarco-distilbert-base-v4
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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-trans... | [
"# sentence-transformers/msmarco-distilbert-base-v4\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 sent... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/msmarco-distilbert-base-v4\n\nThis is a sentence-transformers model: It maps sentences & paragra... |
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="DBusAI/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "F... | DBusAI/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T10:25:46+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GideonFr/q-FrozenLake-v1-4x4-non_slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-non_slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type"... | GideonFr/q-FrozenLake-v1-4x4-non_slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T10:29:18+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"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_small_NCC_lm | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints... | null | 2022-05-21T10:44:41+00:00 | [
"2104.09617",
"1910.10683"
] | [
"no",
"nn",
"sv",
"dk",
"is",
"en"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="CWhy/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribute... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "F... | CWhy/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T10:44:44+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"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "other", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e6re <... | north/t5_small_NCC_modern | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"license:other",
"autotrain_compatible",
"endpoints_comp... | null | 2022-05-21T10:44:53+00:00 | [
"2104.09617",
"1910.10683"
] | [
"no",
"nn",
"sv",
"dk",
"is",
"en"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_small_NCC | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints... | null | 2022-05-21T10:45:05+00:00 | [
"2104.09617",
"1910.10683"
] | [
"no",
"nn",
"sv",
"dk",
"is",
"en"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_base_NCC_lm | null | [
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"endpoints... | null | 2022-05-21T10:45:24+00:00 | [
"2104.09617",
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] | [
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
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] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "other", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e6re <... | north/t5_base_NCC_modern | null | [
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"2104.09617",
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
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] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_base_NCC | null | [
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"2104.09617",
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
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] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_large_NCC_lm | null | [
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"dataset:wikipedia",
"arxiv:2104.09617",
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"license:apache-2.0",
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"2104.09617",
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] | [
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
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] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "other", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e6re <... | north/t5_large_NCC_modern | null | [
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"2104.09617",
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] | [
"no",
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] | TAGS
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
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] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_large_NCC | null | [
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"2104.09617",
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] | [
"no",
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] | TAGS
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_xl_NCC_lm | null | [
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"license:apache-2.0",
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"2104.09617",
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "other", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e6re <... | north/t5_xl_NCC_modern | null | [
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"arxiv:2104.09617",
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"endpoints_compatible",... | null | 2022-05-21T10:46:48+00:00 | [
"2104.09617",
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"no",
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_xl_NCC | null | [
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"license:apache-2.0",
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"2104.09617",
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] | [
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_xxl_NCC_lm | null | [
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"... | null | 2022-05-21T10:47:06+00:00 | [
"2104.09617",
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"no",
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| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e... | north/t5_xxl_NCC | null | [
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"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"... | null | 2022-05-21T10:47:12+00:00 | [
"2104.09617",
"1910.10683"
] | [
"no",
"nn",
"sv",
"dk",
"is",
"en"
] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | GPT2 345M trained on 30 years of CentraBank's speeches | {} | ThePixOne/gptcb | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-21T10:57:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT2 345M trained on 30 years of CentraBank's speeches | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #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="DBusAI/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 +/... | DBusAI/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T10:57:25+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
# German Hotel Review Sentiment Classification
A model trained on German Hotel Reviews from Switzerland. The base model is the [bert-base-german-cased](https://huggingface.co/bert-base-german-cased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then t... | {"language": "de", "license": "apache-2.0", "tags": ["bert"], "widget": [{"text": "Das Fr\u00fchst\u00fcck ist sehr gut, es gibt auch Laktosefreie Produkte.", "example_title": "Example 1"}, {"text": "Das Personal ist sehr kompetent und sehr freundlich.", "example_title": "Example 2"}, {"text": "Die Zimmer sind wie besc... | Tobias/bert-base-uncased_German_MultiLable_classification | null | [
"transformers",
"tf",
"bert",
"text-classification",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T11:00:43+00:00 | [] | [
"de"
] | TAGS
#transformers #tf #bert #text-classification #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| German Hotel Review Sentiment Classification
============================================
A model trained on German Hotel Reviews from Switzerland. The base model is the bert-base-german-cased. The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then traine... | [] | [
"TAGS\n#transformers #tf #bert #text-classification #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \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. -->
# rob2rand_chen_w_prefix_tc
This model is a fine-tuned version of [imamnurby/rob2rand_chen_w_prefix](https://huggingface.co/imamnu... | {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "rob2rand_chen_w_prefix_tc", "results": []}]} | imamnurby/rob2rand_chen_w_prefix_tc | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T11:11:26+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| rob2rand\_chen\_w\_prefix\_tc
=============================
This model is a fine-tuned version of imamnurby/rob2rand\_chen\_w\_prefix on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2749
* Bleu: 83.9120
* Em: 86.2159
* Bleu Em: 85.0639
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #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: 5e-06\n* train\\_batch\\_size: 64\n* eval\\_b... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-8x8", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_s... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "metrics": [{... | DBusAI/q-FrozenLake-v1-8x8 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T11:15:44+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-8x8-no_slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-no_slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type":... | DBusAI/q-FrozenLake-v1-8x8-no_slippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T11:23:26+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | DBusAI/q-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T11:29:04+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-4x4-slippery-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "... | DBusAI/q-FrozenLake-v1-4x4-slippery-v1 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T11:31:24+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DBusAI/q-FrozenLake-v1-4x4-slippery-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "... | DBusAI/q-FrozenLake-v1-4x4-slippery-v3 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T11:37:29+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-crypto-tweet-sentiment-test2
This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "finetuning-crypto-tweet-sentiment-test2", "results": []}]} | laurens88/finetuning-crypto-tweet-sentiment-test2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T11:44:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-crypto-tweet-sentiment-test2
This model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
"# finetuning-crypto-tweet-sentiment-test2\n\nThis model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore ... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-crypto-tweet-sentiment-test2\n\nThis model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset.",
... |
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="DBusAI/q-FrozenLake-v1-8x8-slippery-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8", "type": "FrozenLake-v1-8x8"}, "... | DBusAI/q-FrozenLake-v1-8x8-slippery-v3 | null | [
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T11:45:01+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | Dani-91/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 | 2022-05-21T11:47:18+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: 0.0618
* Precision: 0.9325
* Recall: 0.9487
* F1: 0.9405
* Accuracy: 0.9860
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-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... | Akshat/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T12:11:34+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: 0.2246
* Accuracy: 0.922
* F1: 0.9216
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | Leizhang/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T12:16:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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="DBusAI/q-Taxi-v3-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.80... | DBusAI/q-Taxi-v3-v1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T12:30:51+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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="DBusAI/q-Taxi-v3-v2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "9.12... | DBusAI/q-Taxi-v3-v2 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T12:32:41+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | domcermak/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T12:34:58+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... |
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="DBusAI/q-Taxi-v3-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "10.2... | DBusAI/q-Taxi-v3-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T12:40:21+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null | Ароматерапия София. Аромамасаж. Масаж с етерични масла.
Ароматерапия. Аромамасаж София. Ароматерапия в София. АРОМА Масаж с натурални етерични масла. [https://www.padmalaya.bg/masaji-v-sofia/aromaterapiya-masaj-eterichni-masla-8/](https://www.padmalaya.bg/masaji-v-sofia/aromaterapiya-masaj-eterichni-masla-8/) | {} | padmalaya/padmalaya | null | [
"region:us"
] | null | 2022-05-21T12:41:28+00:00 | [] | [] | TAGS
#region-us
| Ароматерапия София. Аромамасаж. Масаж с етерични масла.
Ароматерапия. Аромамасаж София. Ароматерапия в София. АРОМА Масаж с натурални етерични масла. URL | [] | [
"TAGS\n#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="DBusAI/q-Taxi-v3-v4", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "10.5... | DBusAI/q-Taxi-v3-v4 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T12:43:49+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Redaction Classifier: NLP Edition
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/micr... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "nlp-redaction-classifier", "results": []}]} | strickvl/nlp-redaction-classifier | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T12:57:46+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Redaction Classifier: NLP Edition
=================================
This model is a fine-tuned version of microsoft/deberta-v3-small on a custom dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0893
* Pearson: 0.8273
Model description
-----------------
Read more about the process and ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Photons/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attrib... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "F... | Photons/q-FrozenLake-v1-8x8-slippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T13:00:26+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 |
# **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... | GKPro/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T13:03:57+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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="DBusAI/q-Taxi-v3-v5", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "11.7... | DBusAI/q-Taxi-v3-v5 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T13:04:32+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | forsc/unit12ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T13:08:46+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... |
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="NeutralBlaster/q-FrozenLake-v1-8x8-no_slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additio... | {"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-no_slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type":... | NeutralBlaster/q-FrozenLake-v1-8x8-no_slippery | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T13:29:29+00:00 | [] | [] | TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | 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... | mmartu/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T13:43:57+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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="rmn0ff/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attribu... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | rmn0ff/q-FrozenLake-v1-4x4-slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T13:54:50+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# mt5-small-indonesian-sum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an [cseb... | {"language": ["id"], "license": ["apache-2.0"], "tags": ["Summarization", "mT5"], "datasets": ["csebuetnlp/xlsum"]} | assamim/mt5-small-indonesian | null | [
"transformers",
"tf",
"tensorboard",
"mt5",
"text2text-generation",
"Summarization",
"mT5",
"id",
"dataset:csebuetnlp/xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-21T13:57:36+00:00 | [] | [
"id"
] | TAGS
#transformers #tf #tensorboard #mt5 #text2text-generation #Summarization #mT5 #id #dataset-csebuetnlp/xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-indonesian-sum
========================
This model is a fine-tuned version of google/mt5-small on an csebuetnlp/xlsum dataset.
Training results
----------------
Training hyperparameters
------------------------
* training\_precision: float32
Using this model in 'transformers' (tested on 4.19.2)
--... | [] | [
"TAGS\n#transformers #tf #tensorboard #mt5 #text2text-generation #Summarization #mT5 #id #dataset-csebuetnlp/xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | wooihen/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T14:06:07+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rmn0ff/q-FrozenLake-v1-4x4", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_s... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake... | rmn0ff/q-FrozenLake-v1-4x4 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-05-21T14:14:49+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 |
# **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)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 inst... | {"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... | BioGeek/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-21T14:27:53+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
Then, you can use the model like this:
| [
"# 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)\n\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\nThen, you can use the model like this:"
] | [
"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)\n\nUsing this ... |
text2text-generation | transformers |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"language": [false, "nn", "sv", "dk", "is", "en"], "license": "other", "datasets": ["nbailab/NCC", "mc4", "wikipedia"], "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsr\u00e5d p\u00e5 <extra_id_1>. Dette organet er \u00f8verste <extra_id_2> i Norge. For at m\u00f8tet skal v\u00e6re <... | north/byt5_base_NCC | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"license:other",
"autotrain_compatible",
"endpoints_comp... | null | 2022-05-21T14:35:50+00:00 | [
"2104.09617",
"1910.10683"
] | [
"no",
"nn",
"sv",
"dk",
"is",
"en"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible T5 and T5X and can be used for a variety of NLP tasks ranging from classification to translation.
T5X Checkpoint
--------------
The original T5X checkpoint is also available for this model in the Go... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #no #nn #sv #dk #is #en #dataset-nbailab/NCC #dataset-mc4 #dataset-wikipedia #arxiv-2104.09617 #arxiv-1910.10683 #license-other #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. -->
# vc-bantai-vit-withoutAMBI
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/googl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "model-index": [{"name": "vc-bantai-vit-withoutAMBI", "results": []}]} | AykeeSalazar/vc-bantai-vit-withoutAMBI | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-21T14:37:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vc-bantai-vit-withoutAMBI
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2578
- eval_accuracy: 0.9533
- eval_runtime: 31.6691
- eval_samples_per_second: 130.443
- eval_steps_per_second: 2.... | [
"# vc-bantai-vit-withoutAMBI\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2578\n- eval_accuracy: 0.9533\n- eval_runtime: 31.6691\n- eval_samples_per_second: 130.443\n- eval_steps_per_... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# vc-bantai-vit-withoutAMBI\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_f... |
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