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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. -->
# roberta-base-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/d... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "roberta-base-squad2-finetuned-squad", "results": []}]} | msms/roberta-base-squad2-finetuned-squad | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T08:17:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
| roberta-base-squad2-finetuned-squad
===================================
This model is a fine-tuned version of deepset/roberta-base-squad2 on the squad\_v2 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tf #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-cc-by-4.0 #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\... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras"} | JmPaunlagui/Improve | null | [
"keras",
"region:us"
] | null | 2022-08-02T08:42:09+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Tarkan/cikolata-finetuned-hastalik
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Tarkan/cikolata-finetuned-hastalik", "results": []}]} | Tarkan/cikolata-finetuned-hastalik | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T09:07:32+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Tarkan/cikolata-finetuned-hastalik
==================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1853
* Validation Loss: 0.0921
* Train Precision: 0.6410
* Train Recall: 0.7388
* Train ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 339, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightD... |
text-classification | transformers |
# Dynamically quantized DistilBERT base uncased finetuned SST-2
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetune... | {"language": "en", "license": "apache-2.0", "tags": ["neural-compressor", "text-classfication", "int8", "8-bit", "onnx", "Intel\u00ae Neural Compressor"], "datasets": ["sst2", "glue"], "metrics": ["accuracy"]} | Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic | null | [
"transformers",
"pytorch",
"onnx",
"distilbert",
"text-classification",
"neural-compressor",
"text-classfication",
"int8",
"8-bit",
"Intel® Neural Compressor",
"en",
"dataset:sst2",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T09:16:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #distilbert #text-classification #neural-compressor #text-classfication #int8 #8-bit #Intel® Neural Compressor #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Dynamically quantized DistilBERT base uncased finetuned SST-2
=============================================================
Table of Contents
-----------------
* Model Details
* How to Get Started With the Model
Model Details
-------------
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynami... | [
"### PyTorch\n\n\nTo load the quantized model, you can do as follows:",
"### ONNX\n\n\nThis is an INT8 ONNX model quantized with Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model DistilBERT.",
"#### Test result",
"#### Load ONNX model:"
] | [
"TAGS\n#transformers #pytorch #onnx #distilbert #text-classification #neural-compressor #text-classfication #int8 #8-bit #Intel® Neural Compressor #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nTo load the quantized model, you can ... |
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-cased-distilled-squad-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased-distilled-squ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2_yash"], "model-index": [{"name": "distilbert-base-cased-distilled-squad-finetuned-squad", "results": []}]} | yashwantk/distilbert-base-cased-distilled-squad-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2_yash",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T09:29:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2_yash #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-cased-distilled-squad-finetuned-squad
=====================================================
This model is a fine-tuned version of distilbert-base-cased-distilled-squad on the squad\_v2\_yash dataset.
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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2_yash #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\\_b... |
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... | mrm8488/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-02T09:36:01+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{... | spacestar1705/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-02T09:50:12+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-wikiandmark_epoch50
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wikiandmark_epoch50", "results": []}]} | leokai/distilbert-base-uncased-finetuned-wikiandmark_epoch50 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T10:02:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-wikiandmark_epoch50
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0049
- eval_accuracy: 0.9995
- eval_runtime: 29.1585
- eval_samples_per_second: 127.613
- eval_steps_pe... | [
"# distilbert-base-uncased-finetuned-wikiandmark_epoch50\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0049\n- eval_accuracy: 0.9995\n- eval_runtime: 29.1585\n- eval_samples_per_second: 127.613\n- eva... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-wikiandmark_epoch50\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknow... |
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. -->
# msms/deberta-v3-base-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/deberta-v3-base-squad2](https://huggingface... | {"license": "cc-by-4.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "msms/deberta-v3-base-squad2-finetuned-squad", "results": []}]} | msms/deberta-v3-base-squad2-finetuned-squad | null | [
"transformers",
"tf",
"deberta-v2",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T10:28:16+00:00 | [] | [] | TAGS
#transformers #tf #deberta-v2 #question-answering #generated_from_keras_callback #license-cc-by-4.0 #endpoints_compatible #region-us
| msms/deberta-v3-base-squad2-finetuned-squad
===========================================
This model is a fine-tuned version of deepset/deberta-v3-base-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7266
* Validation Loss: 4.5755
* Epoch: 1
Model description
-... | [
"### 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': 5e-05, 'decay\\_steps': 1533, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #deberta-v2 #question-answering #generated_from_keras_callback #license-cc-by-4.0 #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': 'Polyn... |
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. -->
# lmv2-g-invoice-993-doc-08-02
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/mi... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-invoice-993-doc-08-02", "results": []}]} | Sebabrata/lmv2-g-invoice-993-doc-08-02 | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T10:30:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| lmv2-g-invoice-993-doc-08-02
============================
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3517
* Due Date Precision: 0.9277
* Due Date Recall: 0.875
* Due Date F1: 0.9006
* Due Date Number... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 30",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* tra... |
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. -->
# en-fr-translation
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "en-fr-translation", "results": []}]} | LawalAfeez/en-fr-translation | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T11:30:18+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| en-fr-translation
=================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.7838
* Validation Loss: 1.5505
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'na... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_reg2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_reg2", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_reg2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T11:31:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_reg2
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7256
* Mse: 0.7256
* Mae: 0.6674
* R2: 0.4579
* Accuracy: 0.4573
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelco... | spacestar1705/Reinforce-PixelCopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-02T11:45:24+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glu... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "roberta-base-finetuned-cola", "results": []}]} | anjleeg/roberta-base-finetuned-cola | null | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T11:51:41+00:00 | [] | [] | TAGS
#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-cola
===========================
This model is a fine-tuned version of roberta-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4497
* Matthews Correlation: 0.6272
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: IPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_s... | [
"TAGS\n#transformers #pytorch #optimum_graphcore #roberta #text-classification #generated_from_trainer #dataset-glue #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | Petros89/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T12:00:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0611
* Precision: 0.9320
* Recall: 0.9487
* F1: 0.9403
* Accuracy: 0.9861
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | aliromagnoli/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T12:13:25+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: 0.2145
* Accuracy: 0.924
* F1: 0.9239
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
reinforcement-learning | stable-baselines3 |
# **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... | BekirTaha/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-02T12:34:41+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... |
video-classification | transformers |
# VideoMAE (base-sized model, fine-tuned on Kinetics-400)
VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://ar... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base-short-finetuned-kinetics | null | [
"transformers",
"pytorch",
"videomae",
"video-classification",
"vision",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T12:44:02+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, fine-tuned on Kinetics-400)
VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et a... | [
"# VideoMAE (base-sized model, fine-tuned on Kinetics-400) \n\nVideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Ton... | [
"TAGS\n#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, fine-tuned on Kinetics-400) \n\nVideoMAE model pre-trained for 800 epochs in a self-supervised way and fin... |
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. -->
# leabum/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "leabum/distilbert-base-uncased-finetuned-squad", "results": []}]} | leabum/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T12:48:08+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| leabum/distilbert-base-uncased-finetuned-squad
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.5824
* Train End Logits Accuracy: 0.0347
* Train Start Logits Ac... | [
"### 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': 18, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name'... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #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\\... |
null | null |
WideResNet50 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia,... | {"license": "mit"} | FluxML/wideresnet50 | null | [
"license:mit",
"region:us"
] | null | 2022-08-02T12:58:42+00:00 | [] | [] | TAGS
#license-mit #region-us
|
WideResNet50 model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
WideResNet101 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia... | {"license": "mit"} | FluxML/wideresnet101 | null | [
"license:mit",
"region:us"
] | null | 2022-08-02T13:02:29+00:00 | [] | [] | TAGS
#license-mit #region-us
|
WideResNet101 model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
video-classification | transformers |
# VideoMAE (large-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and ... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-large | null | [
"transformers",
"pytorch",
"videomae",
"pretraining",
"vision",
"video-classification",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T13:02:31+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (large-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository.
... | [
"# VideoMAE (large-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this reposit... | [
"TAGS\n#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (large-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-s... |
video-classification | transformers |
# VideoMAE (large-sized model, fine-tuned on Kinetics-400)
VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-large-finetuned-kinetics | null | [
"transformers",
"pytorch",
"videomae",
"video-classification",
"vision",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T13:09:56+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (large-sized model, fine-tuned on Kinetics-400)
VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et... | [
"# VideoMAE (large-sized model, fine-tuned on Kinetics-400) \n\nVideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by T... | [
"TAGS\n#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (large-sized model, fine-tuned on Kinetics-400) \n\nVideoMAE model pre-trained for 1600 epochs in a self-supervised way and f... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1212245840
- CO2 Emissions (in grams): 2.3558
## Validation Metrics
- Loss: 0.018
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["BenWord/autotrain-data-APM2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 2.355843472980154}} | BenWord/autotrain-APM2-1212245840 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:BenWord/autotrain-data-APM2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T13:11:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-BenWord/autotrain-data-APM2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1212245840
- CO2 Emissions (in grams): 2.3558
## Validation Metrics
- Loss: 0.018
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000
## Usage
You can use cURL to access this model:
Or Python API:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1212245840\n- CO2 Emissions (in grams): 2.3558",
"## Validation Metrics\n\n- Loss: 0.018\n- Accuracy: 1.000\n- Precision: 1.000\n- Recall: 1.000\n- AUC: 1.000\n- F1: 1.000",
"## Usage\n\nYou can use cURL to access this model:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-BenWord/autotrain-data-APM2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1212245840\n- CO2 Emissions (in grams): ... |
video-classification | transformers |
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Something-Something-v2 for 800 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et ... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base-short-ssv2 | null | [
"transformers",
"pytorch",
"videomae",
"pretraining",
"vision",
"video-classification",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T13:15:11+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Something-Something-v2 for 800 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repos... | [
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Something-Something-v2 for 800 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this... | [
"TAGS\n#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Something-Something-v2 for 800 epochs in ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | ligerre/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T13:16:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1343
* F1: 0.8637
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_1_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_1_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_1_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T13:33:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_1\_ternary\_v1
=============================================
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: 2.1145
* F1: 0.7757
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | ligerre/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T13:48:44+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1654
* F1: 0.8590
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\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. -->
# deberta-base-finetuned-wnli
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/debe... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-base-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "wnli", "split": "train... | jinghan/deberta-base-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T13:56:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-base-finetuned-wnli
===========================
This model is a fine-tuned version of microsoft/deberta-base on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6926
* Accuracy: 0.5634
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | ligerre/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T14:15:14+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2848
* F1: 0.8299
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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/1256841238298292232/ycqw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/iamsamirarora-naval-vivek_investor/1659453403535/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/iamsamirarora-naval-vivek_investor | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T14:15:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Naval & Samir Arora & Vivek
@iamsamirarora-naval-vivek\_investor
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 t... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | ligerre/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T14:32:21+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2401
* F1: 0.8246
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | ligerre/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T14:48:23+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3932
* F1: 0.7032
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | ligerre/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T15:04:16+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1757
* F1: 0.8513
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\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_fold_2_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_2_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_2_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T15:13:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_2\_ternary\_v1
=============================================
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: 1.8941
* F1: 0.7889
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config"... | Christabel/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T15:17:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3093
- Accuracy: 0.86
- F1: 0.8636
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3093\n- Accuracy: 0.86\n- F1: 0.8636",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
video-classification | transformers |
# VideoMAE (base-sized model, fine-tuned on Something-Something-v2)
VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base-short-finetuned-ssv2 | null | [
"transformers",
"pytorch",
"videomae",
"video-classification",
"vision",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T15:17:19+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, fine-tuned on Something-Something-v2)
VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-T... | [
"# VideoMAE (base-sized model, fine-tuned on Something-Something-v2) \n\nVideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video... | [
"TAGS\n#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, fine-tuned on Something-Something-v2) \n\nVideoMAE model pre-trained for 800 epochs in a self-supervised w... |
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/623318993290440704/5r2Zw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/metaprophet/1659457657587/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/metaprophet | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T15:26:28+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Meta Prophet
@metaprophet
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_3_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_3_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_3_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T15:27:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_3\_ternary\_v1
=============================================
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: 1.8908
* F1: 0.7879
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_battle2** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_battle2", "type": "doom_battle2"}, "metrics": [{"t... | andrewzhang505/sample-factory-2-doom-battle2 | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-02T15:33:35+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_battle2 environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #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_fold_4_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_4_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_4_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T15:42:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_4\_ternary\_v1
=============================================
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: 1.9355
* F1: 0.7891
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# indobert-hoax-classification
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indob... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "indobert-hoax-classification", "results": []}]} | Rifky/indobert-hoax-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T15:42:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| indobert-hoax-classification
============================
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6230
* Accuracy: 0.8059
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.2173070213315e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* seed: 30\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 4.2173070213315e-05\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_5_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_5_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_5_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T15:56:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_5\_ternary\_v1
=============================================
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: 2.1368
* F1: 0.7682
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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... | mrm8488/dqn-SpaceInvadersNoFrameskip-v4-2 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-02T15:59:39+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
video-classification | transformers |
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Something-Something-v2 for 2400 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base-ssv2 | null | [
"transformers",
"pytorch",
"videomae",
"pretraining",
"vision",
"video-classification",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T16:00:34+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Something-Something-v2 for 2400 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repo... | [
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Something-Something-v2 for 2400 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in thi... | [
"TAGS\n#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Something-Something-v2 for 2400 epochs in... |
video-classification | transformers |
# VideoMAE (base-sized model, fine-tuned on Something-Something-v2)
VideoMAE model pre-trained for 2400 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base-finetuned-ssv2 | null | [
"transformers",
"pytorch",
"videomae",
"video-classification",
"vision",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-02T16:06:22+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, fine-tuned on Something-Something-v2)
VideoMAE model pre-trained for 2400 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-... | [
"# VideoMAE (base-sized model, fine-tuned on Something-Something-v2) \n\nVideoMAE model pre-trained for 2400 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Vide... | [
"TAGS\n#transformers #pytorch #videomae #video-classification #vision #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, fine-tuned on Something-Something-v2) \n\nVideoMAE model pre-trained for 2400 epochs in a self-supervised ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_6_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_6_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_6_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T16:11:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_6\_ternary\_v1
=============================================
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: 1.9031
* F1: 0.7910
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess
This model is a fine-tuned version of [digitalepidemiologylab/cov... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess", "results": []}]} | sumba/covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T16:16:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| covid-twitter-bert-v2-no\_description-stance-loss-hyp-unprocess
===============================================================
This model is a fine-tuned version of digitalepidemiologylab/covid-twitter-bert-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5162
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.4275469935864394e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.4275469935864394e-05\n* train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_7_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_7_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_7_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T16:26:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_7\_ternary\_v1
=============================================
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: 2.0462
* F1: 0.7836
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_8_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_8_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_8_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T16:40:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_8\_ternary\_v1
=============================================
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: 1.8474
* F1: 0.8022
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
reinforcement-learning | null |
# **PPO** Agent playing
This is a trained model of a ***PPO** agent playing CartPole-v1.
To learn to use this model and train yours check Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
{'exp_name': 'ppo', 'seed': 1, 'torch_deterministic': True, 'c... | {"tags": ["CartPole-v1", "ppo", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "CleanRLTest", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_re... | osanseviero/CleanRLTest | null | [
"CartPole-v1",
"ppo",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-02T16:45:02+00:00 | [] | [] | TAGS
#CartPole-v1 #ppo #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# PPO Agent playing
This is a trained model of a *PPO agent playing CartPole-v1.
To learn to use this model and train yours check Unit 8 of the Deep Reinforcement Learning Class: URL
{'exp_name': 'ppo', 'seed': 1, 'torch_deterministic': True, 'cuda': True, 'track': False, 'wandb_project_name': 'cleanRL', 'wan... | [
"# PPO Agent playing \n This is a trained model of a *PPO agent playing CartPole-v1.\n To learn to use this model and train yours check Unit 8 of the Deep Reinforcement Learning Class: URL\n {'exp_name': 'ppo', 'seed': 1, 'torch_deterministic': True, 'cuda': True, 'track': False, 'wandb_project_name': 'cleanRL',... | [
"TAGS\n#CartPole-v1 #ppo #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# PPO Agent playing \n This is a trained model of a *PPO agent playing CartPole-v1.\n To learn to use this model and train yours check Unit 8 of the Deep Reinforcement Learning Class: URL\n {'ex... |
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... | carsepmo/firstRLmodel_LL | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-02T16:47:23+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | comodoro/Worm-Unity-ML | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-08-02T16:50:32+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
#... | [
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
null | null | About this DeepLearning Model:
We will build an front end application to upload the image and get the deeplearning model predicts the name of the object with acccuracy.
Steps for building the Image classification model:
1. Image classification model using pretrained DL model
1.1 Define deeplearning model
2.2 Preproces... | {} | ArunkumarCH/DeepLearning | null | [
"region:us"
] | null | 2022-08-02T16:53:42+00:00 | [] | [] | TAGS
#region-us
| About this DeepLearning Model:
We will build an front end application to upload the image and get the deeplearning model predicts the name of the object with acccuracy.
Steps for building the Image classification model:
1. Image classification model using pretrained DL model
1.1 Define deeplearning model
2.2 Preproces... | [
"# import required modules\nimport json\nimport numpy as np\nfrom PIL import Image\nimport URL as plt",
"# import pytorch related modules\nimport torch\nfrom torchvision import transforms\nfrom URL import densenet121",
"# define pretrained DL model\nmodel = densenet121(pretrained=True)\n\nURL();\n1.2 Preprocess... | [
"TAGS\n#region-us \n",
"# import required modules\nimport json\nimport numpy as np\nfrom PIL import Image\nimport URL as plt",
"# import pytorch related modules\nimport torch\nfrom torchvision import transforms\nfrom URL import densenet121",
"# define pretrained DL model\nmodel = densenet121(pretrained=True)\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_9_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_9_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_9_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T16:54:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_9\_ternary\_v1
=============================================
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: 1.9406
* F1: 0.7841
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment-5Epochs
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknow... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentiment-5Epochs", "results": []}]} | sepidmnorozy/sentiment-5Epochs | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T16:58:38+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sentiment-5Epochs
=================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4947
* Accuracy: 0.8719
* F1: 0.8685
* Precision: 0.8919
* Recall: 0.8463
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* e... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_10_ternary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_10_ternary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_10_ternary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T17:09:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_10\_ternary\_v1
==============================================
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: 1.9887
* F1: 0.7797
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-generation | transformers |
# Naruto DialoGPT Model | {"tags": ["conversational"]} | DogH2O/DialoGPT-small-naruto | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T17:51:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Naruto DialoGPT Model | [
"# Naruto DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Naruto DialoGPT Model"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | QuickSilver007/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-02T17:55:13+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
null | null |
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
mak... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | bhadi26/hadi-rebecca-test-model-public | null | [
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"license:mit",
"region:us"
] | null | 2022-08-02T18:37:11+00:00 | [
"1907.11692",
"1806.02847"
] | [
"en"
] | TAGS
#exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1907.11692 #arxiv-1806.02847 #license-mit #region-us
| RoBERTa base model
==================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERTa ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content ... | [
"TAGS\n#exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1907.11692 #arxiv-1806.02847 #license-mit #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955
## Validation Metrics
- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: ... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["aujer/autotrain-data-not_interested_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.695519133475222}} | aujer/autotrain-not_interested_2-1213045881 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:aujer/autotrain-data-not_interested_2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T20:14:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955
## Validation Metrics
- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1213045881\n- CO2 Emissions (in grams): 1.6955",
"## Validation Metrics\n\n- Loss: 1.607\n- Accuracy: 0.535\n- Macro F1: 0.306\n- Micro F1: 0.535\n- Weighted F1: 0.440\n- Macro Precision: 0.346\n- Micro Precision: 0.535\n-... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1213045881\n- CO2 Emissio... |
text-generation | transformers |
# Po from kung fu panda | {"tags": ["conversational"]} | NoPeanuts/DialoGPT-small-po | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T20:22:59+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Po from kung fu panda | [
"# Po from kung fu panda"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Po from kung fu panda"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1213145894
- CO2 Emissions (in grams): 1.5490
## Validation Metrics
- Loss: 0.904
- Accuracy: 0.735
- Macro F1: 0.566
- Micro F1: 0.735
- Weighted F1: 0.715
- Macro Precision: 0.566
- Micro Precision: 0.735
- Weighted Precision: ... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["aujer/autotrain-data-not_interested_1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.5489539045493725}} | aujer/autotrain-not_interested_1-1213145894 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:aujer/autotrain-data-not_interested_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T20:26:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1213145894
- CO2 Emissions (in grams): 1.5490
## Validation Metrics
- Loss: 0.904
- Accuracy: 0.735
- Macro F1: 0.566
- Micro F1: 0.735
- Weighted F1: 0.715
- Macro Precision: 0.566
- Micro Precision: 0.735
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1213145894\n- CO2 Emissions (in grams): 1.5490",
"## Validation Metrics\n\n- Loss: 0.904\n- Accuracy: 0.735\n- Macro F1: 0.566\n- Micro F1: 0.735\n- Weighted F1: 0.715\n- Macro Precision: 0.566\n- Micro Precision: 0.735\n-... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1213145894\n- CO2 Emis... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_1_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_1_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_1_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T20:49:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_1\_binary\_v1
============================================
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: 1.7296
* F1: 0.8038
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_2_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_2_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_2_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T21:03:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_2\_binary\_v1
============================================
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: 1.8833
* F1: 0.7841
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_3_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_3_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_3_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T21:19:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_3\_binary\_v1
============================================
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: 1.9405
* F1: 0.7878
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_4_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_4_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_4_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T21:34:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_4\_binary\_v1
============================================
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: 1.5144
* F1: 0.8245
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_5_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_5_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_5_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T21:48:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_5\_binary\_v1
============================================
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: 1.6980
* F1: 0.8110
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_6_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_6_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_6_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T22:03:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_6\_binary\_v1
============================================
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: 1.7209
* F1: 0.8156
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_7_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_7_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_7_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-02T22:18:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_7\_binary\_v1
============================================
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: 1.8361
* F1: 0.7958
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-generation | transformers |
# Homer Simpson | {"tags": ["conversational"]} | Gravitygaming/homerai | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T23:41:33+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Homer Simpson | [
"# Homer Simpson"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Homer Simpson"
] |
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. -->
# python-bytes-distilgpt2
This model is not affiliated with the Python Bytes podcast in any way.
This model is a fine-tuned versi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "fastAPI is a great new web framework to easily build web APIs.", "example_title": "fastAPI"}, {"text": "The new tool uses sqlite for performance", "example_title": "sqlite"}], "model-index": [{"name": "python-byt... | arvkevi/python-bytes-distilgpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-02T23:48:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# python-bytes-distilgpt2
This model is not affiliated with the Python Bytes podcast in any way.
This model is a fine-tuned version of distilgpt2 on Python Bytes show notes.
It achieves the following results on the evaluation set:
- Loss: 3.0372
- Accuracy: 0.3969
## Model description
This model generates conve... | [
"# python-bytes-distilgpt2\n\nThis model is not affiliated with the Python Bytes podcast in any way.\n\nThis model is a fine-tuned version of distilgpt2 on Python Bytes show notes.\n\nIt achieves the following results on the evaluation set:\n- Loss: 3.0372\n- Accuracy: 0.3969",
"## Model description\n\nThis model... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# python-bytes-distilgpt2\n\nThis model is not affiliated with the Python Bytes podcast in any way.\n\nThis model is... |
null | null | this is labelled as "poor-man's clip" in a very old google colab i found, transformers==4.21.0 required
some old incomplete code i found to maybe run this:
```python
from torch import nn, optim
import torch
import os
import pandas as pd
import torch.nn.functional as F
class CFG:
debug = False
device = torc... | {} | crumb/pmclip-test-run-checkpoints-10 | null | [
"region:us"
] | null | 2022-08-03T00:12:43+00:00 | [] | [] | TAGS
#region-us
| this is labelled as "poor-man's clip" in a very old google colab i found, transformers==4.21.0 required
some old incomplete code i found to maybe run this:
loading, probably?
| [] | [
"TAGS\n#region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vc-bantai-vit-withoutAMBI-adunest-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vc-bantai-vit-withoutAMBI-adunest-v3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefol... | AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T00:15:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vc-bantai-vit-withoutAMBI-adunest-v3
====================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8889
* Accuracy: 0.8218
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 200\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
null | diffusers |
# VirtualPetDiffusion2
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on a dataset of roughly 8,000 virtual pet thumbnail images.
## Intended uses & limitations
This model can be used to generate small (128x128) virtual pet-like thum... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "Qilex/private_guys", "metrics": []} | Qilex/VirtualPetDiffusion2 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:Qilex/private_guys",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-03T00:51:06+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-Qilex/private_guys #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# VirtualPetDiffusion2
## Model description
This diffusion model is trained with the Diffusers library
on a dataset of roughly 8,000 virtual pet thumbnail images.
## Intended uses & limitations
This model can be used to generate small (128x128) virtual pet-like thumbnails.
The pets are generally somewhat abstra... | [
"# VirtualPetDiffusion2",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non a dataset of roughly 8,000 virtual pet thumbnail images.",
"## Intended uses & limitations\n\nThis model can be used to generate small (128x128) virtual pet-like thumbnails.\nThe pets are generall... | [
"TAGS\n#diffusers #tensorboard #en #dataset-Qilex/private_guys #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us \n",
"# VirtualPetDiffusion2",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non a dataset of roughly 8,000 virtual pet thumbnail images.",
"... |
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. -->
# my_bean_VIT
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-pat... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "model-index": [{"name": "my_bean_VIT", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "config": "default", "split": "train"... | woojinSong/my_bean_VIT | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T03:20:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| my\_bean\_VIT
=============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0321
* Accuracy: 0.9925
Model description
-----------------
Bean datasets based Vision Transformer model.
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #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\\_ra... |
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. -->
# abyaugustinek/distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "abyaugustinek/distilbert-base-uncased-finetuned", "results": []}]} | abyaugustinek/distilbert-base-uncased-finetuned | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T03:41:55+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| abyaugustinek/distilbert-base-uncased-finetuned
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.3693
* Validation Loss: 1.2106
* Train Precision: 0.0
* Train ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 30, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': Fa... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightD... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniPhoBert-v1
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
Mor... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "MiniPhoBert-v1", "results": []}]} | keepitreal/MiniPhoBert-v1 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T03:54:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# MiniPhoBert-v1
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyp... | [
"# MiniPhoBert-v1\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hype... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# MiniPhoBert-v1\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nM... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | ArneD/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T05:20:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4884
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
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. -->
# ner-distilber-cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "ner-distilber-cased", "results": []}]} | rjac/ner-distilbert-cased | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T05:33:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# ner-distilber-cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pr... | [
"# ner-distilber-cased\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ... | [
"TAGS\n#transformers #pytorch #tf #distilbert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# ner-distilber-cased\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mode... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft1500_reg3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_reg3", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_reg3 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T06:33:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_reg3
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7954
* Mse: 0.7954
* Mae: 0.6900
* R2: 0.4769
* Accuracy: 0.4459
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-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. -->
# chinese-roberta-wwm-ext-finetuned
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "chinese-roberta-wwm-ext-finetuned", "results": []}]} | zhiguoxu/chinese-roberta-wwm-ext-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T06:41:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-roberta-wwm-ext-finetuned
=================================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2139
* Accuracy: 1.0
* F1: 1.0
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\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 #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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... | kws/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-03T06:42:45+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. -->
# chinese-roberta-wwm-ext-finetuned2
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "chinese-roberta-wwm-ext-finetuned2", "results": []}]} | zhiguoxu/chinese-roberta-wwm-ext-finetuned2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T06:54:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| chinese-roberta-wwm-ext-finetuned2
==================================
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1448
* Accuracy: 1.0
* F1: 1.0
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* e... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
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 import... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | 29thDay/PPO-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-03T07:41:13+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
video-classification | transformers |
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and f... | {"license": "cc-by-nc-4.0", "tags": ["vision", "video-classification"]} | MCG-NJU/videomae-base | null | [
"transformers",
"pytorch",
"videomae",
"pretraining",
"vision",
"video-classification",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-03T08:27:59+00:00 | [
"2203.12602",
"2111.06377"
] | [] | TAGS
#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# VideoMAE (base-sized model, pre-trained only)
VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository.
... | [
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this reposito... | [
"TAGS\n#transformers #pytorch #videomae #pretraining #vision #video-classification #arxiv-2203.12602 #arxiv-2111.06377 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# VideoMAE (base-sized model, pre-trained only) \n\nVideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-su... |
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. -->
# distilroberta-base-wikitextepoch_50
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilrobe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-wikitextepoch_50", "results": []}]} | leokai/distilroberta-base-wikitextepoch_50 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T08:57:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-wikitextepoch\_50
====================================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6360
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Trainin... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-fine-tuned-cola", "results": []}]} | SlavaC/bert-fine-tuned-cola | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T09:12:13+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-fine-tuned-cola
====================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2861
* Validation Loss: 0.4212
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_r... |
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. -->
# MLM-For-MarkTuin-400
This model was trained from scratch on the None dataset.
## Model description
More information needed
##... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "MLM-For-MarkTuin-400", "results": []}]} | DioLiu/MLM-For-MarkTuin-400 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T09:19:58+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# MLM-For-MarkTuin-400
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperp... | [
"# MLM-For-MarkTuin-400\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperpa... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# MLM-For-MarkTuin-400\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | masapasa/is_cat | null | [
"fastai",
"region:us"
] | null | 2022-08-03T09:53:01+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
translation | transformers |
### opus-mt-fr-en
* source languages: fr
* target languages: en
* OPUS readme: [fr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-02-26.zip](http... | {"license": "apache-2.0", "tags": ["translation"]} | Sampreetha/opus-mt-fr-en | null | [
"transformers",
"pytorch",
"jax",
"marian",
"text2text-generation",
"translation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T10:10:29+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #marian #text2text-generation #translation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### opus-mt-fr-en
* source languages: fr
* target languages: en
* OPUS readme: fr-en
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: URL
* test set translations: URL
* test set scores: URL
Benchmarks
----------
testset: URL, BLEU: 33.1, chr-F... | [
"### opus-mt-fr-en\n\n\n* source languages: fr\n* target languages: en\n* OPUS readme: fr-en\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: URL\n* test set translations: URL\n* test set scores: URL\n\n\nBenchmarks\n----------\n\n\ntestset:... | [
"TAGS\n#transformers #pytorch #jax #marian #text2text-generation #translation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### opus-mt-fr-en\n\n\n* source languages: fr\n* target languages: en\n* OPUS readme: fr-en\n* dataset: opus\n* model: transformer-align\n* pre-processing:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_8_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_8_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_8_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T10:22:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_8\_binary\_v1
============================================
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: 1.6283
* F1: 0.8178
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_9_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_9_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_9_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T10:37:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_9\_binary\_v1
============================================
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: 1.6965
* F1: 0.8090
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers | # Romanian paraphrase
Fine-tune t5-small model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v1). The dataset contains ~60k examples.
### ... | {"language": ["ro"], "license": ["apache-2.0"], "tags": [], "annotations_creators": [], "language_creators": ["machine-generated"], "multilinguality": ["monolingual"], "pretty_name": "BlackKakapo/t5-small-paraphrase-ro", "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-... | BlackKakapo/t5-small-paraphrase-ro | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ro",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T10:37:59+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Romanian paraphrase
!v1.0
Fine-tune t5-small model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~60k examples.
### How to use
### Or
### Generate
### Output
| [
"# Romanian paraphrase\n\n!v1.0\n\nFine-tune t5-small model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~60k examples.",
"### How to use",
"### Or",
"### Generate",
"### Output"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Romanian paraphrase\n\n!v1.0\n\nFine-tune t5-small model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Rookie-06/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T10:48:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
null | null |
This is a weights storage for models trained by [ReadingPipeline](https://github.com/ai-forever/ReadingPipeline)
The weights are for ocr and segmentations models trained on [school notebooks dataset](https://huggingface.co/datasets/sberbank-ai/school_notebooks_RU)
| {"language": ["ru"], "license": "mit", "tags": ["PyTorch", "OCR", "Segmentation", "HTR"], "datasets": ["sberbank-ai/school_notebooks_RU", "sberbank-ai/school_notebooks_EN"]} | ai-forever/ReadingPipeline-notebooks | null | [
"onnx",
"PyTorch",
"OCR",
"Segmentation",
"HTR",
"ru",
"dataset:sberbank-ai/school_notebooks_RU",
"dataset:sberbank-ai/school_notebooks_EN",
"license:mit",
"region:us"
] | null | 2022-08-03T10:49:24+00:00 | [] | [
"ru"
] | TAGS
#onnx #PyTorch #OCR #Segmentation #HTR #ru #dataset-sberbank-ai/school_notebooks_RU #dataset-sberbank-ai/school_notebooks_EN #license-mit #region-us
|
This is a weights storage for models trained by ReadingPipeline
The weights are for ocr and segmentations models trained on school notebooks dataset
| [] | [
"TAGS\n#onnx #PyTorch #OCR #Segmentation #HTR #ru #dataset-sberbank-ai/school_notebooks_RU #dataset-sberbank-ai/school_notebooks_EN #license-mit #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_fold_10_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_10_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_10_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T10:51:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased\_fold\_10\_binary\_v1
=============================================
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: 1.6912
* F1: 0.7977
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-CTC Large (Croatian)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [![L... | {"language": ["hr"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard", "Riva"], "datasets": ["ParlaSpeech-HR-v1.0"]} | nvidia/stt_hr_conformer_ctc_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"CTC",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"Riva",
"hr",
"dataset:ParlaSpeech-HR-v1.0",
"arxiv:2005.08100",
"license:cc-by-4.0",
"region:us"
] | null | 2022-08-03T10:54:54+00:00 | [
"2005.08100"
] | [
"hr"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #hr #dataset-ParlaSpeech-HR-v1.0 #arxiv-2005.08100 #license-cc-by-4.0 #region-us
| NVIDIA Conformer-CTC Large (Croatian)
=====================================
img {
display: inline;
}
| 
| 
| 
|  |
This model transcribes speech in lowercase Croatian ... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nSimply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16 kHz single-channel audio as input.",
"### Output\n\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n\nModel... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #CTC #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #Riva #hr #dataset-ParlaSpeech-HR-v1.0 #arxiv-2005.08100 #license-cc-by-4.0 #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nSimply do:",
"###... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-nli
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xnli"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-nli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "xnli", "type": "xnli", "config": "... | MayaGalvez/bert-base-multilingual-cased-finetuned-nli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:xnli",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T10:58:59+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-xnli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-multilingual-cased-finetuned-nli
==========================================
This model is a fine-tuned version of bert-base-multilingual-cased on the xnli dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4681
* Accuracy: 0.8157
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-xnli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PL... | Saraswati/Reinforce-CartPole-v1 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-03T11:03:32+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinf... |
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