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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | jayeshgar/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T05:53:42+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# MiniLM-L12-H384-uncased-mrpc
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/mi... | {"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "MiniLM-L12-H384-uncased-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "... | Intel/MiniLM-L12-H384-uncased-mrpc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T05:55:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# MiniLM-L12-H384-uncased-mrpc
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4319
- Accuracy: 0.875
- F1: 0.9097
- Combined Score: 0.8924
## Model description
More information needed
## Intended... | [
"# MiniLM-L12-H384-uncased-mrpc\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4319\n- Accuracy: 0.875\n- F1: 0.9097\n- Combined Score: 0.8924",
"## Model description\n\nMore information need... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniLM-L12-H384-uncased-mrpc\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the GLUE MRPC datas... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | pinku/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-10T06:18:59+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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",... | flood/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T06:19:14+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-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: 0.7793
* Accuracy: 0.9161
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
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. -->
# layoutlmv3-finetuned-invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/... | {"tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "args": "sroie"}... | ronak1998/layoutlmv3-finetuned-invoice | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T06:27:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv3-finetuned-invoice
============================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0030
* Precision: 1.0
* Recall: 0.9980
* F1: 0.9990
* Accuracy: 0.9998
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 2000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* ... |
text-classification | transformers |
# INT8 MiniLM-L12-H384 finetuned MRPC
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp... | {"language": "en", "license": "mit", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["mrpc"], "metrics": ["f1"]} | Intel/MiniLM-L12-H384-uncased-mrpc-int8-static | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"text-classification",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"en",
"dataset:mrpc",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T06:34:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us
| INT8 MiniLM-L12-H384 finetuned MRPC
===================================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\nThe original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.\n\n\nThe calibration dataloader is the train dataloader. The default calibration sampli... | [
"TAGS\n#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel... |
null | transformers |
This is the large variant of FinBERT (TurkuNLP/bert-base-finnish-cased-v1). The training data is exactly the same. | {"language": "fi", "license": "apache-2.0"} | TurkuNLP/bert-large-finnish-cased-v1 | null | [
"transformers",
"pytorch",
"fi",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T06:53:16+00:00 | [] | [
"fi"
] | TAGS
#transformers #pytorch #fi #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
This is the large variant of FinBERT (TurkuNLP/bert-base-finnish-cased-v1). The training data is exactly the same. | [] | [
"TAGS\n#transformers #pytorch #fi #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | flood/distilbert-base-uncased-distilled-clinc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T06:59:25+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0389
* Accuracy: 0.9310
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# led-base-16384-finetuned-big_patent
This model was trained from scratch on an unknown dataset.
It achieves the following results on th... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "led-base-16384-finetuned-big_patent", "results": []}]} | robingeibel/led-base-16384-finetuned-big_patent | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"led",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T07:18:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
|
# led-base-16384-finetuned-big_patent
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed... | [
"# led-base-16384-finetuned-big_patent\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nM... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #led #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us \n",
"# led-base-16384-finetuned-big_patent\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model ... |
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. -->
# amazon_shoe_reviews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "amazon_shoe_reviews", "results": []}]} | th4tkh13m/amazon_shoe_reviews | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T07:43:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# amazon_shoe_reviews
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparamet... | [
"# amazon_shoe_reviews\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure"... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# amazon_shoe_reviews\n\nThis model is a fine-tuned version of distilbert-base-uncased on the Non... |
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/1522249702837657603/1jNZ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/atrioc/1654851931751/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/atrioc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T07:58:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Atrioc
@atrioc
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
-------------
Th... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ksabeh/bert-base-uncased-attribute-correction-mlm-titles
This model is a fine-tuned version of [ksabeh/bert-base-uncased-attribute-cor... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ksabeh/bert-base-uncased-attribute-correction-mlm-titles", "results": []}]} | ksabeh/bert-base-uncased-attribute-correction-mlm-titles | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T08:02:24+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| ksabeh/bert-base-uncased-attribute-correction-mlm-titles
========================================================
This model is a fine-tuned version of ksabeh/bert-base-uncased-attribute-correction-mlm on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0430
* Validation ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 23878, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Polynomial... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | YaYaB/SpaceInvadersNoFrameskip-v4-2 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T08:15:44+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | keras |
## Model description
In this, GauGAN architecture has been implemented for conditional image generation which was proposed in [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://arxiv.org/abs/1903.07291).
GauGAN uses a `Generative Adversarial Network (GAN)` to generate realistic images that are... | {"library_name": "keras", "tags": ["ImageGeneration", "GauGAN", "GAN", "spatially-adaptive normalization", "Encoder", "Segmentation-maps"]} | keras-io/GauGAN-Image-generation | null | [
"keras",
"tensorboard",
"ImageGeneration",
"GauGAN",
"GAN",
"spatially-adaptive normalization",
"Encoder",
"Segmentation-maps",
"arxiv:1903.07291",
"has_space",
"region:us"
] | null | 2022-06-10T08:31:07+00:00 | [
"1903.07291"
] | [] | TAGS
#keras #tensorboard #ImageGeneration #GauGAN #GAN #spatially-adaptive normalization #Encoder #Segmentation-maps #arxiv-1903.07291 #has_space #region-us
| Model description
-----------------
In this, GauGAN architecture has been implemented for conditional image generation which was proposed in Semantic Image Synthesis with Spatially-Adaptive Normalization.
GauGAN uses a 'Generative Adversarial Network (GAN)' to generate realistic images that are conditioned on cue i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\n\nModel Reproduced By <a href=\"URL Bisht</b>"
] | [
"TAGS\n#keras #tensorboard #ImageGeneration #GauGAN #GAN #spatially-adaptive normalization #Encoder #Segmentation-maps #arxiv-1903.07291 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Mod... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-finetuned-triviaqa
This model is a fine-tuned version of [FabianWillner/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad-finetuned-triviaqa", "results": []}]} | FabianWillner/distilbert-base-uncased-finetuned-squad-finetuned-triviaqa | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T08:44:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad-finetuned-triviaqa
==========================================================
This model is a fine-tuned version of FabianWillner/distilbert-base-uncased-finetuned-squad on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9583
Model descr... | [
"### 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: 2",
"### Traini... | [
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"### 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... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned", "results": []}]} | stig/distilbert-base-uncased-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T08:59:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned
=================================
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: 1.8627
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 #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval... |
text2text-generation | transformers | https://github.com/kranti-gloify/grammarly/tree/Django_API_Final
| {} | Jayaprakash/Grammar_correction | null | [
"transformers",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T09:00:53+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| URL
| [] | [
"TAGS\n#transformers #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# INT8 MiniLM finetuned MRPC
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine... | {"language": "en", "license": "mit", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "QuantizationAwareTraining"], "datasets": ["mrpc"], "metrics": ["f1"]} | Intel/MiniLM-L12-H384-uncased-mrpc-int8-qat | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"QuantizationAwareTraining",
"en",
"dataset:mrpc",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T09:08:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #QuantizationAwareTraining #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us
| INT8 MiniLM finetuned MRPC
==========================
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.
### Test result
... | [
"### QuantizationAwareTraining\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model Intel/MiniLM-L12-H384-uncased-mrpc.",
"### Test result",
"### Load with optimum:",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #QuantizationAwareTraining #en #dataset-mrpc #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### QuantizationAwareTraining\n\n\nThis is an INT8 PyTorch model quantized with huggin... |
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. -->
# deep-haiku-gpt-j-6b-8bit
This model is a fine-tuned version of [gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit) on... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deep-haiku-gpt-j-6b-8bit", "results": []}]} | fabianmmueller/deep-haiku-gpt-j-6b-8bit | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T09:10:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gptj #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# deep-haiku-gpt-j-6b-8bit
This model is a fine-tuned version of gpt-j-6B-8bit on the haiku dataset.
## Model description
The model is a fine-tuned version of GPT-J-6B-8Bit for generation of Haikus. The model, data and training procedure is inspired by a blog post by Robert A. Gonsalves.
We used the same multita... | [
"# deep-haiku-gpt-j-6b-8bit\n\nThis model is a fine-tuned version of gpt-j-6B-8bit on the haiku dataset.",
"## Model description\n\nThe model is a fine-tuned version of GPT-J-6B-8Bit for generation of Haikus. The model, data and training procedure is inspired by a blog post by Robert A. Gonsalves.\n\nWe used the ... | [
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"# deep-haiku-gpt-j-6b-8bit\n\nThis model is a fine-tuned version of gpt-j-6B-8bit on the haiku dataset.",
"## Model description\n\nThe model is a fine... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | shivigupta/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T09:10:35+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
object-detection | keras |
## Model description
Implementing RetinaNet: Focal Loss for Dense Object Detection.
This repo contains the model for the notebook [**Object Detection with RetinaNet**](https://keras.io/examples/vision/retinanet/)
Here the model is tasked with localizing the objects present in an image, and at the same time, classif... | {"library_name": "keras", "tags": ["ObjectDetection", "RetinaNet", "ResNet50", "ObjectClassification", "Feature Pyramid Network"], "pipeline_tag": "object-detection"} | keras-io/Object-Detection-RetinaNet | null | [
"keras",
"tensorboard",
"ObjectDetection",
"RetinaNet",
"ResNet50",
"ObjectClassification",
"Feature Pyramid Network",
"object-detection",
"arxiv:1708.02002",
"arxiv:1612.03144",
"has_space",
"region:us"
] | null | 2022-06-10T09:11:44+00:00 | [
"1708.02002",
"1612.03144"
] | [] | TAGS
#keras #tensorboard #ObjectDetection #RetinaNet #ResNet50 #ObjectClassification #Feature Pyramid Network #object-detection #arxiv-1708.02002 #arxiv-1612.03144 #has_space #region-us
| Model description
-----------------
Implementing RetinaNet: Focal Loss for Dense Object Detection.
This repo contains the model for the notebook Object Detection with RetinaNet
Here the model is tasked with localizing the objects present in an image, and at the same time, classifying them into different categorie... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\n\nModel Reproduced By <a href=\"URL Bisht</b>"
] | [
"TAGS\n#keras #tensorboard #ObjectDetection #RetinaNet #ResNet50 #ObjectClassification #Feature Pyramid Network #object-detection #arxiv-1708.02002 #arxiv-1612.03144 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\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="danieladejumo/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | danieladejumo/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-10T09:25:23+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# assamim/mt5-pukulenam-summarization
This model is a fine-tuned version of [T5-Small](https://huggingface.co/t5-small) on an [XSUM](https:... | {"tags": ["generated_from_keras_callback", "Summarization", "T5-Small"], "datasets": ["Xsum"], "model-index": [{"name": "assamim/mt5-pukulenam-summarization", "results": []}]} | assamim/t5-small-english | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"Summarization",
"T5-Small",
"dataset:Xsum",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T09:42:01+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #Summarization #T5-Small #dataset-Xsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# assamim/mt5-pukulenam-summarization
This model is a fine-tuned version of T5-Small on an XSUM dataset
## Using this model in 'transformers' (tested on 4.19.2)
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1 | [
"# assamim/mt5-pukulenam-summarization\nThis model is a fine-tuned version of T5-Small on an XSUM dataset",
"## Using this model in 'transformers' (tested on 4.19.2)",
"### Framework versions\n\n- Transformers 4.19.2\n- TensorFlow 2.8.2\n- Datasets 2.2.2\n- Tokenizers 0.12.1"
] | [
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"# assamim/mt5-pukulenam-summarization\nThis model is a fine-tuned version of T5-Small on an ... |
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. -->
# distilrubert-2ndfinetune-epru
This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_best_fine... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-2ndfinetune-epru", "results": []}]} | mmillet/distilrubert-2ndfinetune-epru | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T09:49:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert-2ndfinetune-epru
=============================
This model is a fine-tuned version of mmillet/distilrubert-tiny-cased-conversational-v1\_best\_finetuned\_emotion\_experiment\_augmented\_anger\_fear on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3531
* Accuracy:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-vios-commonvoice-1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-vios-commonvoice-1", "results": []}]} | tclong/wav2vec2-base-vios-commonvoice-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T10:09:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-vios-commonvoice-1
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8913
* Wer: 0.3621
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNAPerceiver1_2epochs
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the fol... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DNAPerceiver1_2epochs", "results": []}]} | simecek/DNAPerceiver1_2epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"perceiver",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T10:38:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #perceiver #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| DNAPerceiver1\_2epochs
======================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3330
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More inf... | [
"### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #perceiver #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | ghpkishore/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T10:51:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2183
* Accuracy: 0.9285
* F1: 0.9285
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned_personality_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned_personality_multi", "results": []}]} | titi7242229/roberta-base-bne-finetuned_personality_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T10:55:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned\_personality\_multi
==============================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3709
* Accuracy: 0.5130
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 1e-05\n* train\\_batc... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-arxiv
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-arxiv", "results": []}]} | becher/t5-small-finetuned-arxiv | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T10:59:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-arxiv
========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1559
* Rouge1: 37.854
* Rouge2: 20.4934
* Rougel: 33.9992
* Rougelsum: 33.9943
* Gen Len: 15.847
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
automatic-speech-recognition | transformers |
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model has apostrophes and hyphens.
Metrics:
| Dataset | CER | WER |
|-|-|-|
| CV7 (no LM) | 0.0432 | 0.2288 |
| CV7 (with LM) | 0.0267 | 0.128... | {"language": ["uk"], "license": "cc-by-sa-3.0", "datasets": ["mozilla-foundation/common_voice_10_0"]} | Yehor/wav2vec2-xls-r-300m-uk-with-wiki-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"uk",
"dataset:mozilla-foundation/common_voice_10_0",
"license:cc-by-sa-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T11:03:09+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-sa-3.0 #endpoints_compatible #region-us
| 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk
⭐ See other Ukrainian models - URL
This model has apostrophes and hyphens.
Metrics:
Dataset: CV7 (no LM), CER: 0.0432, WER: 0.2288
Dataset: CV7 (with LM), CER: 0.0267, WER: 0.1283
Dataset: CV10 (no LM), CER: 0.0412, WER: 0.2... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #uk #dataset-mozilla-foundation/common_voice_10_0 #license-cc-by-sa-3.0 #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **Ant-v3**
This is a trained model of a **A2C** agent playing **Ant-v3**
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
reinforcement lear... | {"library_name": "stable-baselines3", "tags": ["Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Ant-v3", "type": "Ant-v3"}, "metrics": [... | sb3/a2c-Ant-v3 | null | [
"stable-baselines3",
"Ant-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T11:12:32+00:00 | [] | [] | TAGS
#stable-baselines3 #Ant-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing Ant-v3
This is a trained model of a A2C agent playing Ant-v3
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 (with SB3 RL Zoo)
RL... | [
"# A2C Agent playing Ant-v3\nThis is a trained model of a A2C agent playing Ant-v3\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.",
"## Usage (with ... | [
"TAGS\n#stable-baselines3 #Ant-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing Ant-v3\nThis is a trained model of a A2C agent playing Ant-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\n... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **Walker2d-v3**
This is a trained model of a **A2C** agent playing **Walker2d-v3**
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
reinforc... | {"library_name": "stable-baselines3", "tags": ["Walker2d-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2d-v3", "type": "Walker2d-v3"... | sb3/a2c-Walker2d-v3 | null | [
"stable-baselines3",
"Walker2d-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T11:18:08+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing Walker2d-v3
This is a trained model of a A2C agent playing Walker2d-v3
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 (with SB3 R... | [
"# A2C Agent playing Walker2d-v3\nThis is a trained model of a A2C agent playing Walker2d-v3\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.",
"## Us... | [
"TAGS\n#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing Walker2d-v3\nThis is a trained model of a A2C agent playing Walker2d-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **Humanoid-v3**
This is a trained model of a **A2C** agent playing **Humanoid-v3**
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
reinforc... | {"library_name": "stable-baselines3", "tags": ["Humanoid-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Humanoid-v3", "type": "Humanoid-v3"... | sb3/a2c-Humanoid-v3 | null | [
"stable-baselines3",
"Humanoid-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T11:19:45+00:00 | [] | [] | TAGS
#stable-baselines3 #Humanoid-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing Humanoid-v3
This is a trained model of a A2C agent playing Humanoid-v3
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 (with SB3 R... | [
"# A2C Agent playing Humanoid-v3\nThis is a trained model of a A2C agent playing Humanoid-v3\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.",
"## Us... | [
"TAGS\n#stable-baselines3 #Humanoid-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing Humanoid-v3\nThis is a trained model of a A2C agent playing Humanoid-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
text-generation | transformers |
# House MD DialoGPT Model | {"tags": ["conversational"]} | daedalus2003/HouseBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T11:20:21+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# House MD DialoGPT Model | [
"# House MD DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# House MD DialoGPT Model"
] |
text2text-generation | transformers |
## Introduction
[Google's LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) introduced as an extension of a successful [T5 model](https://arxiv.org/pdf/1910.10683.pdf).
This is an unofficial *longt5-large-16384-pubmed-3k_steps* checkpoint. I.e., this is a large conf... | {"language": "en", "license": "apache-2.0", "datasets": ["ccdv/pubmed-summarization"]} | Stancld/longt5-tglobal-large-16384-pubmed-3k_steps | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"longt5",
"text2text-generation",
"en",
"dataset:ccdv/pubmed-summarization",
"arxiv:2112.07916",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T11:24:12+00:00 | [
"2112.07916",
"1910.10683"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #longt5 #text2text-generation #en #dataset-ccdv/pubmed-summarization #arxiv-2112.07916 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Introduction
------------
Google's LongT5: Efficient Text-To-Text Transformer for Long Sequences introduced as an extension of a successful T5 model.
This is an unofficial *longt5-large-16384-pubmed-3k\_steps* checkpoint. I.e., this is a large configuration of the LongT5 model with a 'transient-global' attention fi... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #longt5 #text2text-generation #en #dataset-ccdv/pubmed-summarization #arxiv-2112.07916 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
image-classification | keras |
## Model description
### Consistency training with supervision
[Keras Example Link](https://keras.io/examples/vision/consistency_training/)
In this example, we have trained an image classification model enforcing a sense of consistency inside it by doing the following:
- Train a standard image classification model... | {"library_name": "keras", "tags": ["image-classification", "computer-vision", "consistency-regularization", "cifar10"]} | keras-io/consistency_training_with_supervision_teacher_model | null | [
"keras",
"tensorboard",
"image-classification",
"computer-vision",
"consistency-regularization",
"cifar10",
"has_space",
"region:us"
] | null | 2022-06-10T11:36:40+00:00 | [] | [] | TAGS
#keras #tensorboard #image-classification #computer-vision #consistency-regularization #cifar10 #has_space #region-us
| Model description
-----------------
### Consistency training with supervision
Keras Example Link
In this example, we have trained an image classification model enforcing a sense of consistency inside it by doing the following:
* Train a standard image classification model.
* Train an equal or larger model on a ... | [
"### Consistency training with supervision\n\n\nKeras Example Link\n\n\nIn this example, we have trained an image classification model enforcing a sense of consistency inside it by doing the following:\n\n\n* Train a standard image classification model.\n* Train an equal or larger model on a noisy version of the da... | [
"TAGS\n#keras #tensorboard #image-classification #computer-vision #consistency-regularization #cifar10 #has_space #region-us \n",
"### Consistency training with supervision\n\n\nKeras Example Link\n\n\nIn this example, we have trained an image classification model enforcing a sense of consistency inside it by doi... |
image-classification | keras |
## Model description
### Consistency training with supervision
[Keras Example Link](https://keras.io/examples/vision/consistency_training/)
In this example, we have trained an image classification model enforcing a sense of consistency inside it by doing the following:
- Train a standard image classification model... | {"library_name": "keras", "tags": ["image-classification", "computer-vision", "consistency-regularization", "cifar10"]} | keras-io/consistency_training_with_supervision_student_model | null | [
"keras",
"tensorboard",
"image-classification",
"computer-vision",
"consistency-regularization",
"cifar10",
"has_space",
"region:us"
] | null | 2022-06-10T11:44:11+00:00 | [] | [] | TAGS
#keras #tensorboard #image-classification #computer-vision #consistency-regularization #cifar10 #has_space #region-us
| Model description
-----------------
### Consistency training with supervision
Keras Example Link
In this example, we have trained an image classification model enforcing a sense of consistency inside it by doing the following:
* Train a standard image classification model.
* Train an equal or larger model on a ... | [
"### Consistency training with supervision\n\n\nKeras Example Link\n\n\nIn this example, we have trained an image classification model enforcing a sense of consistency inside it by doing the following:\n\n\n* Train a standard image classification model.\n* Train an equal or larger model on a noisy version of the da... | [
"TAGS\n#keras #tensorboard #image-classification #computer-vision #consistency-regularization #cifar10 #has_space #region-us \n",
"### Consistency training with supervision\n\n\nKeras Example Link\n\n\nIn this example, we have trained an image classification model enforcing a sense of consistency inside it by doi... |
feature-extraction | 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. -->
# clip-roberta-finetuned
This model is a fine-tuned version of [./models/clip-roberta](https://huggingface.co/./models/clip-robert... | {"tags": ["generated_from_trainer"], "datasets": ["ydshieh/coco_dataset_script"], "model-index": [{"name": "clip-roberta-finetuned", "results": []}]} | adalbertojunior/clip-rpt | null | [
"transformers",
"pytorch",
"tensorboard",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"dataset:ydshieh/coco_dataset_script",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T11:46:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vision-text-dual-encoder #feature-extraction #generated_from_trainer #dataset-ydshieh/coco_dataset_script #endpoints_compatible #region-us
|
# clip-roberta-finetuned
This model is a fine-tuned version of ./models/clip-roberta on the ydshieh/coco_dataset_script 2017 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7269
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# clip-roberta-finetuned\n\nThis model is a fine-tuned version of ./models/clip-roberta on the ydshieh/coco_dataset_script 2017 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.7269",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #vision-text-dual-encoder #feature-extraction #generated_from_trainer #dataset-ydshieh/coco_dataset_script #endpoints_compatible #region-us \n",
"# clip-roberta-finetuned\n\nThis model is a fine-tuned version of ./models/clip-roberta on the ydshieh/coco_dataset_script 20... |
fill-mask | transformers |
# deberta-large-japanese-unidic
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune `deberta-large-japanese-unidic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-unidic-luw-upos), [dependenc... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]} | KoichiYasuoka/deberta-large-japanese-unidic | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T11:49:12+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-large-japanese-unidic
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune 'deberta-large-japanese-unidic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
fugashi and unidic-lite are required.
| [
"# deberta-large-japanese-unidic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fine-tune 'deberta-large-japanese-unidic' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use\n\n\n\nfugashi and unidic-lit... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-large-japanese-unidic",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts with BertJapaneseTokenizer. You can fi... |
token-classification | transformers |
# deberta-large-japanese-unidic-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-unidic). Every long-unit-word is tagged by [UPOS](https:... | {"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd... | KoichiYasuoka/deberta-large-japanese-unidic-luw-upos | null | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T11:53:45+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-large-japanese-unidic-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-large-japanese-unidic. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) FEATS.
## How to Use
or
fugashi and unidic-l... | [
"# deberta-large-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-large-japanese-unidic. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) FEATS.",
"## How to Use\n\n\n\nor\n\n\n... | [
"TAGS\n#transformers #pytorch #deberta-v2 #token-classification #japanese #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-large-japanese-unidic-luw-upos",
"## Model Description\n\nThis is a DeBERTa(V2) mode... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | RalphX1/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T12:11:26+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# camembert-base-finetuned-LineCause
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base)... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall"], "model-index": [{"name": "camembert-base-finetuned-LineCause", "results": []}]} | louisdeco/camembert-base-finetuned-LineCause | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T12:11:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| camembert-base-finetuned-LineCause
==================================
This model is a fine-tuned version of camembert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0001
* Accuracy: 1.0
* F1: 1.0
* Recall: 1.0
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 50\n* eval\\_batch\\_size: 50\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 #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
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. -->
# juancopi81/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "juancopi81/mt5-small-finetuned-amazon-en-es", "results": []}]} | juancopi81/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T12:57:35+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| juancopi81/mt5-small-finetuned-amazon-en-es
===========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.1238
* Validation Loss: 3.4046
* Epoch: 7
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
text-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-multilingual-cased-finetuned-similarite
This model is a fine-tuned version of [distilbert-base-multilingual-case... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pawsx"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-multilingual-cased-finetuned-similarite", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "pawsx", "type"... | Clody0071/distilbert-base-multilingual-cased-finetuned-similarite | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:pawsx",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T13:33:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-pawsx #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-multilingual-cased-finetuned-similarite
=======================================================
This model is a fine-tuned version of distilbert-base-multilingual-cased on the pawsx dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4781
* Accuracy: 0.7995
* F1: 0.7995
Mod... | [
"### 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-pawsx #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
text-generation | transformers | This model was created by additional training of the giant GPT-3 medium on the works of A.S. Pushkin. Now this model can generate poetry in the style of this poet. Fine-tuning of GPT-3 was produced.
![alt text](https://lh3.googleusercontent.com/73NLTubc1m-Kiz2GJPv44cyMHQgaq32RGr7aWPfsEH5LCpqZxyqtj0TXk6Cw3gjfCzo=w2400... | {} | AnyaSchen/rugpt3_pushkin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T13:45:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was created by additional training of the giant GPT-3 medium on the works of A.S. Pushkin. Now this model can generate poetry in the style of this poet. Fine-tuning of GPT-3 was produced.
!alt text | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #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="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | OTQ/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-10T14:14:51+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"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-arabic-base-finetuned-wikilingua-ar
This model is a fine-tuned version of [bakrianoo/t5-arabic-base](https://huggingface.co/b... | {"license": "apache-2.0", "tags": ["summarization", "mt5", "ar", "abstractive summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "t5-arabic-base-finetuned-wikilingua-ar", "results": []}]} | ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"mt5",
"ar",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region... | null | 2022-06-10T14:19:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #mt5 #ar #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-arabic-base-finetuned-wikilingua-ar
This model is a fine-tuned version of bakrianoo/t5-arabic-base on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2735
- Rouge-1: 20.72
- Rouge-2: 7.63
- Rouge-l: 18.75
- Gen Len: 18.74
- Bertscore: 70.79
## Model description
Mor... | [
"# t5-arabic-base-finetuned-wikilingua-ar\n\nThis model is a fine-tuned version of bakrianoo/t5-arabic-base on the wiki_lingua dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.2735\n- Rouge-1: 20.72\n- Rouge-2: 7.63\n- Rouge-l: 18.75\n- Gen Len: 18.74\n- Bertscore: 70.79",
"## Model d... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #mt5 #ar #abstractive summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-arabic-base-finetuned-wikilingua-ar\... |
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. -->
# camembert-base-finetuned-paraphrase
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["pawsx"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "camembert-base-finetuned-paraphrase", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "pawsx", "type": "pawsx", "args": "fr"}, "... | Clody0071/camembert-base-finetuned-paraphrase | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"dataset:pawsx",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T15:20:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #dataset-pawsx #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| camembert-base-finetuned-paraphrase
===================================
This model is a fine-tuned version of camembert-base on the pawsx dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2708
* Accuracy: 0.9085
* F1: 0.9089
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #dataset-pawsx #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
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 when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | income/bpr-base-msmarco-contriever | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T16:11:14+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
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 can u... | [
"# {MODEL_NAME}\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\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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 se... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | meln1k/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T16:30:14+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# LeoFelix/bert-finetuned-squad
This model is a fine-tuned version of [pierreguillou/bert-base-cased-squad-v1.1-portuguese](https://hugg... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "LeoFelix/bert-finetuned-squad", "results": []}]} | LeoFelix/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T16:58:12+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| LeoFelix/bert-finetuned-squad
=============================
This model is a fine-tuned version of pierreguillou/bert-base-cased-squad-v1.1-portuguese on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0193
* Epoch: 2
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'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/1521909233024913408/4QsF... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/malzliebchen/1654885748305/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/malzliebchen | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T17:26:43+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Malzbeard's Severed Head
@malzliebchen
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... | [] | [
"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/1433527116948180999/wejt... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/smallmutuals/1654888348503/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/smallmutuals | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T17:33:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Cool Owl Guy
@smallmutuals
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/1169751139409117185/BU60... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jana_aych_ess/1654888920998/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jana_aych_ess | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T18:21:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jana 'All Cops Are Bastards' H-S (they/them)
@jana\_aych\_ess
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... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEdetection_distilBERT_mLM_V5
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_V2](https://huggingface.co/Frit... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distilBERT_mLM_V5", "results": []}]} | FritzOS/TEdetection_distilBERT_mLM_V5 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T18:43:11+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TEdetection_distilBERT_mLM_V5
This model is a fine-tuned version of FritzOS/TEdetection_distiBERT_mLM_V2 on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluat... | [
"# TEdetection_distilBERT_mLM_V5\n\nThis model is a fine-tuned version of FritzOS/TEdetection_distiBERT_mLM_V2 on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## ... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TEdetection_distilBERT_mLM_V5\n\nThis model is a fine-tuned version of FritzOS/TEdetection_distiBERT_mLM_V2 on an unknown dataset.\nIt achieves the foll... |
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/1446572046679302144/jF9H... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/ninjasexparty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T18:56:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ninja Sex Party
@ninjasexparty
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. -->
# distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
This model is a fine-tuned version of [DeepPavlov/d... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented", "results": []}]} | mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T19:14:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert-tiny-cased-conversational-v1\_single\_finetuned\_on\_cedr\_augmented
=================================================================================
This model is a fine-tuned version of DeepPavlov/distilrubert-tiny-cased-conversational-v1 on an unknown dataset.
It achieves the following results on the... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEdetection_distiBERT_NER_V5
This model is a fine-tuned version of [FritzOS/TEdetection_distilBERT_mLM_V5](https://huggingface.co/Frit... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEdetection_distiBERT_NER_V5", "results": []}]} | FritzOS/TEdetection_distiBERT_NER_V5 | null | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T19:34:58+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TEdetection\_distiBERT\_NER\_V5
===============================
This model is a fine-tuned version of FritzOS/TEdetection\_distilBERT\_mLM\_V5 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0029
* Validation Loss: 0.0032
* Epoch: 0
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learn... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilrubert-tiny-2ndfinetune-epru
This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_sing... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-tiny-2ndfinetune-epru", "results": []}]} | mmillet/distilrubert-tiny-2ndfinetune-epru | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-10T19:41:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert-tiny-2ndfinetune-epru
==================================
This model is a fine-tuned version of mmillet/distilrubert-tiny-cased-conversational-v1\_single\_finetuned\_on\_cedr\_augmented on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2085
* Accuracy: 0.9333
* F1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* ... |
null | null | git lfs install
git clone https://huggingface.co/torli/trijki | {"license": "artistic-2.0"} | torli/trijki | null | [
"license:artistic-2.0",
"region:us"
] | null | 2022-06-10T19:43:32+00:00 | [] | [] | TAGS
#license-artistic-2.0 #region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#license-artistic-2.0 #region-us \n"
] |
null | null | git lfs install
https://www.novinhavideosporno.com/wp-content/uploads/2018/11/a-maior-buceta-do-mundo-e-a-mais-escrota-tambem.jpg
https://www.xvideos-tv.com/wp-content/uploads/2021/11/buceta-da-novinha-sendo-arrombada-por-varios-machos-272x180.jpg
http://cdn.xvideos-br.com/media/imagens/10501.jpg
https://upload.wikimed... | {} | luisrqe/cubucetapenis | null | [
"region:us"
] | null | 2022-06-10T19:52:33+00:00 | [] | [] | TAGS
#region-us
| git lfs install
URL
URL
URL
URL
URL
URL
git clone URL | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# LFTW R1 Target
The R1 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Datasets to Improve Online H... | {"language": "en"} | facebook/roberta-hate-speech-dynabench-r1-target | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2012.15761",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T20:32:03+00:00 | [
"2012.15761"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LFTW R1 Target
The R1 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub! | [
"# LFTW R1 Target\n\nThe R1 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LFTW R1 Target\n\nThe R1 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kusha... |
text-classification | transformers |
# LFTW R2 Target
The R2 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Datasets to Improve Online H... | {"language": "en"} | facebook/roberta-hate-speech-dynabench-r2-target | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2012.15761",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T20:52:46+00:00 | [
"2012.15761"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LFTW R2 Target
The R2 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub! | [
"# LFTW R2 Target\n\nThe R2 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LFTW R2 Target\n\nThe R2 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kusha... |
text-classification | transformers |
# LFTW R3 Target
The R3 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Datasets to Improve Online H... | {"language": "en"} | facebook/roberta-hate-speech-dynabench-r3-target | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2012.15761",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T21:10:40+00:00 | [
"2012.15761"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LFTW R3 Target
The R3 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub! | [
"# LFTW R3 Target\n\nThe R3 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LFTW R3 Target\n\nThe R3 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kusha... |
text-classification | transformers |
# LFTW R4 Target
The R4 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761)
## Citation Information
```bibtex
@inproceedings{vidgen2021lftw,
title={Learning from the Worst: Dynamically Generated Datasets to Improve Online H... | {"language": "en"} | facebook/roberta-hate-speech-dynabench-r4-target | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"arxiv:2012.15761",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-10T21:24:39+00:00 | [
"2012.15761"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# LFTW R4 Target
The R4 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub! | [
"# LFTW R4 Target\n\nThe R4 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nThanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #arxiv-2012.15761 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LFTW R4 Target\n\nThe R4 Target model from Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection\n\n\n\n\n\nTh... |
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... | antonioricciardi/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-10T21:41:07+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... |
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/1476816918879297559/2jt_... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/boopysaur/1654901824865/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/boopysaur | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T21:56:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
boop
@boopysaur
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"
] |
null | null | # MangaLineExtraction_PyTorch
- https://github.com/ljsabc/MangaLineExtraction_PyTorch
| {} | public-data/MangaLineExtraction_PyTorch | null | [
"region:us",
"has_space"
] | null | 2022-06-10T21:58:25+00:00 | [] | [] | TAGS
#region-us #has_space
| # MangaLineExtraction_PyTorch
- URL
| [
"# MangaLineExtraction_PyTorch\n\n- URL"
] | [
"TAGS\n#region-us #has_space \n",
"# MangaLineExtraction_PyTorch\n\n- URL"
] |
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/1510152678919135250/lfEm... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jedwill1999/1654902604867/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/jedwill1999 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T22:09:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
a local
@jedwill1999
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/1532874424776437760/vSP1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/theanything_bot/1654903166604/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/theanything_bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T22:19:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Anything Bot
@theanything\_bot
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"
] |
null | pytorch |
# modelcard-creator-demo
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use)
- [Limitations a... | {"language": ["en"], "license": "mit", "library_name": "pytorch", "tags": ["modelcards", "autogenerated-modelcard"], "datasets": ["beans"], "metrics": ["accuracy"]} | nateraw/modelcard-creator-demo | null | [
"pytorch",
"modelcards",
"autogenerated-modelcard",
"en",
"dataset:beans",
"arxiv:1810.03993",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2022-06-10T22:40:23+00:00 | [
"1810.03993",
"1910.09700"
] | [
"en"
] | TAGS
#pytorch #modelcards #autogenerated-modelcard #en #dataset-beans #arxiv-1810.03993 #arxiv-1910.09700 #license-mit #region-us
|
# modelcard-creator-demo
## Table of Contents
- Model Details
- How To Get Started With the Model
- Uses
- Direct Use
- Downstream Use
- Misuse and Out of Scope Use
- Limitations and Biases
- Training
- Training Data
- Training Procedure
- Evaluation Results
- Environmental Impact
- Licensing Information
- ... | [
"# modelcard-creator-demo",
"## Table of Contents\n- Model Details\n- How To Get Started With the Model\n- Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out of Scope Use\n- Limitations and Biases\n- Training\n - Training Data\n - Training Procedure\n- Evaluation Results\n- Environmental Impact\n- Lic... | [
"TAGS\n#pytorch #modelcards #autogenerated-modelcard #en #dataset-beans #arxiv-1810.03993 #arxiv-1910.09700 #license-mit #region-us \n",
"# modelcard-creator-demo",
"## Table of Contents\n- Model Details\n- How To Get Started With the Model\n- Uses\n - Direct Use\n - Downstream Use\n - Misuse and Out of Scop... |
null | null | hi bme dred | {} | thebreeder1234/hi | null | [
"region:us"
] | null | 2022-06-10T22:51:38+00:00 | [] | [] | TAGS
#region-us
| hi bme dred | [] | [
"TAGS\n#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/1447692349493100549/1PV2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/froliki2108/1654905851117/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/froliki2108 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T23:02:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Froliki
@froliki2108
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/1447253318380793858/VVNh... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tonebot_/1654906535396/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tonebot_ | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-10T23:14:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
tone bot
@tonebot\_
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"
] |
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. -->
# SCRATCH_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/op... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "SCRATCH_ja-en_helsinki", "results": []}]} | twieland/SCRATCH_ja-en_helsinki | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T00:05:11+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SCRATCH\_ja-en\_helsinki
========================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5583
* Otaku Benchmark VN BLEU: 19.12
* Otaku Benchmark LN BLEU: 11.55
* Otaku Benchmark MANGA BLEU: 12.98
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96\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\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96... |
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/1490538004607385602/laSB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/yomancuso | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T00:08:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Davey Wavey
@yomancuso
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"
] |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-arabic-base-finetuned-xlsum-ar
This model is a fine-tuned version of [bakrianoo/t5-arabic-base](https://huggingface.co/bakria... | {"license": "apache-2.0", "tags": ["summarization", "t5", "ar", "abstractive summarization", "xlsum", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "t5-arabic-base-finetuned-xlsum-ar", "results": []}]} | ahmeddbahaa/t5-arabic-base-finetuned-xlsum-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"ar",
"abstractive summarization",
"xlsum",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"... | null | 2022-06-11T00:21:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #ar #abstractive summarization #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-arabic-base-finetuned-xlsum-ar
This model is a fine-tuned version of bakrianoo/t5-arabic-base on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0328
- Rouge-1: 23.72
- Rouge-2: 10.95
- Rouge-l: 21.59
- Gen Len: 19.0
- Bertscore: 71.81
## Model description
More informati... | [
"# t5-arabic-base-finetuned-xlsum-ar\n\nThis model is a fine-tuned version of bakrianoo/t5-arabic-base on the xlsum dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.0328\n- Rouge-1: 23.72\n- Rouge-2: 10.95\n- Rouge-l: 21.59\n- Gen Len: 19.0\n- Bertscore: 71.81",
"## Model description\... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #ar #abstractive summarization #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-arabic-base-finetuned-xlsum-ar\n\nThis m... |
null | null | katsuki bakugo with a gun | {} | Davstra/sss | null | [
"region:us"
] | null | 2022-06-11T00:23:31+00:00 | [] | [] | TAGS
#region-us
| katsuki bakugo with a gun | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing_ft_pretrain_wav2vec2-large-lv60
This model is a fine-tuned version of [gary109/ai-light-dance_pretrain_wa... | {"tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing_ft_pretrain_wav2vec2-large-lv60", "results": []}]} | gary109/ai-light-dance_singing_ft_pretrain_wav2vec2-large-lv60 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T01:31:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us
| ai-light-dance\_singing\_ft\_pretrain\_wav2vec2-large-lv60
==========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_pretrain\_wav2vec2-large-lv60 on the GARY109/AI\_LIGHT\_DANCE - ONSET-SINGING dataset.
It achieves the following results on the evaluation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rule_learning_margin_1mm_spanpred
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enor... | {"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_1mm_spanpred", "results": []}]} | enoriega/rule_learning_margin_1mm_spanpred | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T01:59:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us
| rule\_learning\_margin\_1mm\_spanpred
=====================================
This model is a fine-tuned version of enoriega/rule\_softmatching on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3250
* Margin Accuracy: 0.8518
Model description
------------... | [
"### 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* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_... |
null | null | BOZO
| {"license": "afl-3.0"} | swordinrock/s | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-06-11T02:30:36+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| BOZO
| [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
feature-extraction | transformers | 在chinese-bert-wwm的基础上进行新闻语料库的增量预训练，token采用的是hfl/chinese-bert-wwm-ext | {} | LDD/bert_wwm_new | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T02:46:18+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| 在chinese-bert-wwm的基础上进行新闻语料库的增量预训练，token采用的是hfl/chinese-bert-wwm-ext | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1534033778787639296/a9JU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/waffle_64/1654922313776/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/waffle_64 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T03:35:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
WerewaffleLOU NATION
@waffle\_64
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
BRATA (Basa Bali Used for Pretraining RoBERTa) is a pretrained language model trained using Basa Bali or Balinese Language with RoBERTa-base-uncased configuration. The datasets used for this pretraining were collected by extracting WikiBali or Wikipedia Basa Bali and some sources from Suara Saking Bali website. The pr... | {"language": "ban", "datasets": ["WikiBali", "Suara Saking Bali"], "widget": [{"text": "Kalsium silih <mask> datu kimia antuk simbol Ca miwah wilangan atom 20.", "example_title": "Conto 1"}, {"text": "Tabuan inggih <mask> silih tunggil soroh beburon sane madue kampid.", "example_title": "Conto 2"}]} | AryaSuprana/BRATA_RoBERTaBali | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"ban",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T03:51:40+00:00 | [] | [
"ban"
] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #ban #autotrain_compatible #endpoints_compatible #region-us
|
BRATA (Basa Bali Used for Pretraining RoBERTa) is a pretrained language model trained using Basa Bali or Balinese Language with RoBERTa-base-uncased configuration. The datasets used for this pretraining were collected by extracting WikiBali or Wikipedia Basa Bali and some sources from Suara Saking Bali website. The pr... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #ban #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned_personality_multi_2
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned_personality_multi_2", "results": []}]} | titi7242229/roberta-base-bne-finetuned_personality_multi_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T04:27:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned\_personality\_multi\_2
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2983
* Accuracy: 0.5429
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 1e-05\n* train\\_batc... |
fill-mask | transformers | A pre-trained Roberta masked language model (MLM) trained on around 12K fake news dataset called LIAR. The perplexity of the original pre-trained Roberta model on the dataset is 5.957 and the perplexity of the adapted model is 3.918. | {} | Jawaher/LIAR-fake-news-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T04:40:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| A pre-trained Roberta masked language model (MLM) trained on around 12K fake news dataset called LIAR. The perplexity of the original pre-trained Roberta model on the dataset is 5.957 and the perplexity of the adapted model is 3.918. | [] | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | SallyXue/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T05:24:22+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned_personality_multi_3
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned_personality_multi_3", "results": []}]} | titi7242229/roberta-base-bne-finetuned_personality_multi_3 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T06:10:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned\_personality\_multi\_3
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1145
* Accuracy: 0.4847
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: 1e-05\n* train\\_batc... |
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. -->
# layoutlmv3-finetuned-wildreceipt
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/micros... | {"tags": ["generated_from_trainer"], "datasets": ["wild_receipt"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-wildreceipt", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wild_receipt", "type": "wild_r... | Theivaprakasham/layoutlmv3-finetuned-wildreceipt | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:wild_receipt",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-11T06:21:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-wild_receipt #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| layoutlmv3-finetuned-wildreceipt
================================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the wild\_receipt dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3108
* Precision: 0.8772
* Recall: 0.8799
* F1: 0.8785
* Accuracy: 0.9249
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* training\\_steps: 4000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-wild_receipt #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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/1535477036353040384/tXI_... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gustholomulers/1654934015981/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gustholomulers | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-11T06:50:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
soppy
@gustholomulers
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 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = g... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | OTQ/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-11T07:10:10+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. -->
# camembert-base-finetuned-RankLineCause
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-b... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall"], "model-index": [{"name": "camembert-base-finetuned-RankLineCause", "results": []}]} | louisdeco/camembert-base-finetuned-RankLineCause | null | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-11T08:02:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| camembert-base-finetuned-RankLineCause
======================================
This model is a fine-tuned version of camembert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3138
* Accuracy: 0.8152
* F1: 0.8297
* Recall: 0.8152
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: 50\n* eval\\_batch\\_size: 50\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 #camembert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
automatic-speech-recognition | adapter-transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Mo... | {"language": ["zh", "ja", "en"], "license": "mit", "library_name": "adapter-transformers", "datasets": ["fka/awesome-chatgpt-prompts", "wikimedia/wikipedia", "unalignment/toxic-dpo-v0.1", "OpenAssistant/oasst2", "m-a-p/COIG-CQIA"], "metrics": ["accuracy", "code_eval", "character"], "pipeline_tag": "automatic-speech-rec... | zuu/automatic-speech-recognition | null | [
"adapter-transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"zh",
"ja",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:wikimedia/wikipedia",
"dataset:unalignment/toxic-dpo-v0.1",
"dataset:OpenAssistant/oasst2",
"dataset:m-a-p/COIG-CQIA",
"arxiv:1910.09700",
"licen... | null | 2022-06-11T08:20:53+00:00 | [
"1910.09700"
] | [
"zh",
"ja",
"en"
] | TAGS
#adapter-transformers #pytorch #wav2vec2 #automatic-speech-recognition #zh #ja #en #dataset-fka/awesome-chatgpt-prompts #dataset-wikimedia/wikipedia #dataset-unalignment/toxic-dpo-v0.1 #dataset-OpenAssistant/oasst2 #dataset-m-a-p/COIG-CQIA #arxiv-1910.09700 #license-mit #region-us
|
# Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model ... | [
"# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- Licens... | [
"TAGS\n#adapter-transformers #pytorch #wav2vec2 #automatic-speech-recognition #zh #ja #en #dataset-fka/awesome-chatgpt-prompts #dataset-wikimedia/wikipedia #dataset-unalignment/toxic-dpo-v0.1 #dataset-OpenAssistant/oasst2 #dataset-m-a-p/COIG-CQIA #arxiv-1910.09700 #license-mit #region-us \n",
"# Model Card for Mo... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | meln1k/qrdqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-11T08:29:19+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
feature-extraction | transformers | 在bert-base-chinese基础上进行新闻语料库的增量预训练的模型，token采用的是hfl/chinese-bert-wwm-ext | {} | LDD/bert_mlm_new | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T08:46:01+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| 在bert-base-chinese基础上进行新闻语料库的增量预训练的模型，token采用的是hfl/chinese-bert-wwm-ext | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilrubert-tiny-2nd-finetune-epru
This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_sin... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilrubert-tiny-2nd-finetune-epru", "results": []}]} | mmillet/distilrubert-tiny-2nd-finetune-epru | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T08:48:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilrubert-tiny-2nd-finetune-epru
===================================
This model is a fine-tuned version of mmillet/distilrubert-tiny-cased-conversational-v1\_single\_finetuned\_on\_cedr\_augmented on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3546
* Accuracy: 0.9325
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* ... |
image-classification | transformers |
# Model vit_base-224-in21k-ft-cifar10
## **A finetuned model for Image classification in Spanish**
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a ... | {"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "vit", "ImageClassification", "generated_from_trainer"], "datasets": ["cifar10"], "metrics": ["accuracy"], "model-index": [{"name": "vit_base-224-in21k-ft-cifar10", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "d... | edumunozsala/vit_base-224-in21k-ft-cifar10 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"sagemaker",
"ImageClassification",
"generated_from_trainer",
"es",
"dataset:cifar10",
"arxiv:2006.03677",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T09:40:44+00:00 | [
"2006.03677"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #sagemaker #ImageClassification #generated_from_trainer #es #dataset-cifar10 #arxiv-2006.03677 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Model vit_base-224-in21k-ft-cifar10
## A finetuned model for Image classification in Spanish
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is Vision Transformer (base-sized model) which is a transformer encoder model (BERT-like) pretrained on a large co... | [
"# Model vit_base-224-in21k-ft-cifar10",
"## A finetuned model for Image classification in Spanish\n\nThis model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,\nThe base model is Vision Transformer (base-sized model) which is a transformer encoder model (BERT-like) pretrained on... | [
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #sagemaker #ImageClassification #generated_from_trainer #es #dataset-cifar10 #arxiv-2006.03677 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model vit_base-224-in21k-ft-cifar10",
"## A finetun... |
image-classification | transformers |
# Model vit_base-224-in21k-ft-cifar100
## **A finetuned model for Image classification in Spanish**
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is **Vision Transformer (base-sized model)** which is a transformer encoder model (BERT-like) pretrained on a... | {"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "vit", "ImageClassification", "generated_from_trainer"], "datasets": ["cifar100"], "metrics": ["accuracy"], "model-index": [{"name": "vit_base-224-in21k-ft-cifar100", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, ... | edumunozsala/vit_base-224-in21k-ft-cifar100 | null | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"sagemaker",
"ImageClassification",
"generated_from_trainer",
"es",
"dataset:cifar100",
"arxiv:2006.03677",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T09:54:37+00:00 | [
"2006.03677"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #sagemaker #ImageClassification #generated_from_trainer #es #dataset-cifar100 #arxiv-2006.03677 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Model vit_base-224-in21k-ft-cifar100
## A finetuned model for Image classification in Spanish
This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is Vision Transformer (base-sized model) which is a transformer encoder model (BERT-like) pretrained on a large c... | [
"# Model vit_base-224-in21k-ft-cifar100",
"## A finetuned model for Image classification in Spanish\n\nThis model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,\nThe base model is Vision Transformer (base-sized model) which is a transformer encoder model (BERT-like) pretrained o... | [
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #sagemaker #ImageClassification #generated_from_trainer #es #dataset-cifar100 #arxiv-2006.03677 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model vit_base-224-in21k-ft-cifar100",
"## A finet... |
unconditional-image-generation | keras |
## Model description
This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules [WGAN-GP with R-GCN for the generation of small molecular graphs](https://keras.io/examples/generative/wgan-graphs/).
Full credits go to [Alexander Kensert](https... | {"library_name": "keras", "tags": ["unconditional-image-generation"]} | keras-io/wgan-molecular-graphs | null | [
"keras",
"tensorboard",
"unconditional-image-generation",
"has_space",
"region:us"
] | null | 2022-06-11T10:25:00+00:00 | [] | [] | TAGS
#keras #tensorboard #unconditional-image-generation #has_space #region-us
|
## Model description
This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.
Full credits go to Alexander Kensert
Reproduced by Vu Minh Chien
Motivation: The development of... | [
"## Model description\n\nThis repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.\n\nFull credits go to Alexander Kensert\n\nReproduced by Vu Minh Chien\n\nMotivation: The de... | [
"TAGS\n#keras #tensorboard #unconditional-image-generation #has_space #region-us \n",
"## Model description\n\nThis repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.\n\nF... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-image_quality
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-image_quality", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder",... | shivarama23/swin-tiny-patch4-window7-224-finetuned-image_quality | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T10:41:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-image\_quality
=====================================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5242
* Accuracy: 0.9091
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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