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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. -->
# Clinical-Longformer-MLM-pubmed
This model is a fine-tuned version of [yikuan8/Clinical-Longformer](https://huggingface.co/yikuan... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Clinical-Longformer-MLM-pubmed", "results": []}]} | Gaborandi/Clinical-Longformer-MLM-pubmed | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"longformer",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T01:00:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #longformer #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Clinical-Longformer-MLM-pubmed
==============================
This model is a fine-tuned version of yikuan8/Clinical-Longformer on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3126
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: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #longformer #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n*... |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | chandrasutrisnotjhong/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T01:01:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8324
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8560\n- Bleu: 52.8324",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | shahma/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T01:02:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | heriosousa/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T02:15:52+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | null | # Introduction
Long sound files for testing streaming ASR in [sherpa](https://github.com/k2-fsa/sherpa).
| {} | csukuangfj/sherpa-long-audio-test-data | null | [
"region:us"
] | null | 2022-06-22T02:22:53+00:00 | [] | [] | TAGS
#region-us
| # Introduction
Long sound files for testing streaming ASR in sherpa.
| [
"# Introduction\n\nLong sound files for testing streaming ASR in sherpa."
] | [
"TAGS\n#region-us \n",
"# Introduction\n\nLong sound files for testing streaming ASR in sherpa."
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny_focal_v2_label
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluati... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_focal_v2_label", "results": []}]} | kktoto/tiny_focal_v2_label | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T02:26:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_focal\_v2\_label
======================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0558
* Precision: 0.6979
* Recall: 0.6747
* F1: 0.6861
* Accuracy: 0.9513
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# veb/twitch-distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "veb/twitch-distilbert-base-uncased-finetuned", "results": []}]} | veb/twitch-distilbert-base-uncased-finetuned | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T02:48:44+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| veb/twitch-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: 4.9110
* Validation Loss: 4.7782
* Epoch: 0
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #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\\_rate'... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-squadshifts-nyt-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.c... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-nyt-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T02:52:43+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-squadshifts-nyt-qg'
==================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* Language:... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data:... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Malaya-speech_fine-tune_realcase_22_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](http... | {"tags": ["generated_from_trainer"], "datasets": ["uob_singlish"], "model-index": [{"name": "Malaya-speech_fine-tune_realcase_22_Jun", "results": []}]} | RuiqianLi/Malaya-speech_fine-tune_realcase_22_Jun | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T03:11:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us
| Malaya-speech\_fine-tune\_realcase\_22\_Jun
===========================================
This model is a fine-tuned version of malay-huggingface/wav2vec2-xls-r-300m-mixed on the uob\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9569
* Wer: 0.4062
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size:... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# veb/twitch-distilbert-base-cased-finetuned
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distil... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "veb/twitch-distilbert-base-cased-finetuned", "results": []}]} | veb/twitch-distilbert-base-cased-finetuned | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T03:18:29+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| veb/twitch-distilbert-base-cased-finetuned
==========================================
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.5140
* Validation Loss: 5.4524
* Epoch: 0
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #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\\_rate'... |
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-continued_training-medqa
This model is a fine-tuned version of [Shaier/distilbert-base-uncased-continued... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-continued_training-medqa", "results": []}]} | Shaier/distilbert-base-uncased-continued_training-medqa | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T03:20:40+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-continued\_training-medqa
=================================================
This model is a fine-tuned version of Shaier/distilbert-base-uncased-continued\_training-medqa on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5389
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 512\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squadshifts-vanilla-nyt-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](h... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-vanilla-nyt-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T03:23:08+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squadshifts-vanilla-nyt-qg'
=====================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language mode... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: l... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T03:33:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53
=====================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2034
* Wer:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 160\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #gary109/AI_Light_Dance #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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. -->
# results
This model is a fine-tuned version of [MRF18/results](https://huggingface.co/MRF18/results) on the None dataset.
## Mod... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]} | MRF18/results | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T03:42:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# results
This model is a fine-tuned version of MRF18/results 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... | [
"# results\n\nThis model is a fine-tuned version of MRF18/results 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 #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# results\n\nThis model is a fine-tuned version of MRF18/results on the None dataset.",
"## Model description\n\nMore information needed",
... |
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. -->
# veb/twitch-bert-base-cased-finetuned
This model was trained from scratch on an unknown dataset.
It achieves the following results on t... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "veb/twitch-bert-base-cased-finetuned", "results": []}]} | veb/twitch-bert-base-cased-finetuned | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T04:06:19+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| veb/twitch-bert-base-cased-finetuned
====================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0952
* Train Sparse Categorical Accuracy: 0.9647
* Validation Loss: 0.0359
* Validation Sparse Categorical Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'bet... |
text-generation | transformers |
# Tony Stark DialoGPT Model | {"tags": ["conversational"]} | prprakash/DialoGPT-small-TonyStark | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T04:09:37+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tony Stark DialoGPT Model | [
"# Tony Stark DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tony Stark DialoGPT Model"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# veb/twitch-bert-base-uncased-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "veb/twitch-bert-base-uncased-finetuned", "results": []}]} | veb/twitch-bert-base-uncased-finetuned | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T04:30:42+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| veb/twitch-bert-base-uncased-finetuned
======================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.0070
* Validation Loss: 4.9998
* Epoch: 0
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #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\\_rate': {'cl... |
image-classification | transformers |
# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)
## Authors:
[Frank Soboczenski](https://h21k.github.io/), University of York & King's College London, UK<br>
[Paul Wright](https://www.wrightai.com/), Wright AI Ltd, Leeds, UK
## General:
This Vision Transformer model has been fine-tuned on Solar Dyn... | {"tags": ["image-classification", "pytorch"], "metrics": ["accuracy"]} | kenobi/SDO_VT1 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"arxiv:2006.03677",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T05:05:49+00:00 | [
"2006.03677"
] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #arxiv-2006.03677 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)
## Authors:
Frank Soboczenski, University of York & King's College London, UK<br>
Paul Wright, Wright AI Ltd, Leeds, UK
## General:
This Vision Transformer model has been fine-tuned on Solar Dynamics Observatory (SDO) data. The images used are availa... | [
"# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)",
"## Authors:\nFrank Soboczenski, University of York & King's College London, UK<br>\nPaul Wright, Wright AI Ltd, Leeds, UK",
"## General:\nThis Vision Transformer model has been fine-tuned on Solar Dynamics Observatory (SDO) data. The images ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #arxiv-2006.03677 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)",
"## Authors:\nFrank Soboczenski, University of York & King's College London, UK<b... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-squadshifts-reddit-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://gi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-reddit-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T05:20:31+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-squadshifts-reddit-qg'
=====================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* ... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data:... |
null | null | Kalyana Virundhu Biryani is one of the best biryani shop in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
https://www.kalyanavirundhubiryani.com/ | {} | kalyanavirundhubiryani/Best-Biryani-Shop-in-Chennai | null | [
"region:us"
] | null | 2022-06-22T05:38:00+00:00 | [] | [] | TAGS
#region-us
| Kalyana Virundhu Biryani is one of the best biryani shop in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
URL | [] | [
"TAGS\n#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. -->
# tiny_no_focal_v2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation ... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_no_focal_v2", "results": []}]} | kktoto/tiny_no_focal_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T05:39:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_no\_focal\_v2
===================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1314
* Precision: 0.7013
* Recall: 0.6837
* F1: 0.6924
* Accuracy: 0.9522
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\... |
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).
## Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spac... | {"license": "apache-2.0", "library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | unity/ML-Agents-Worm | null | [
"ml-agents",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"license:apache-2.0",
"region:us"
] | null | 2022-06-22T05:51:06+00:00 | [] | [] | TAGS
#ml-agents #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #license-apache-2.0 #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.
## Watch your Agent play
You can watch your agent playing directly in your browser:.
1. Go to URL
2. Step 1: Write your model_id: unity/ML-Agents-Worm
3. Step 2: Select your *.nn or *.onnx... | [
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You can watch your agent playing directly in your browser:. \n \n 1. Go to URL\n 2. Step 1: Write your model_id: unity/ML-Agents-Worm\n 3. Step 2: Select your *.... | [
"TAGS\n#ml-agents #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #license-apache-2.0 #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 ## Watch your Agent play\n You can watch your agen... |
null | null | ### Comparison of downsampling methods after 2.5B tokens
| {} | bigscience/dechonk-logs-2 | null | [
"tensorboard",
"region:us"
] | null | 2022-06-22T05:54:49+00:00 | [] | [] | TAGS
#tensorboard #region-us
| ### Comparison of downsampling methods after 2.5B tokens
| [
"### Comparison of downsampling methods after 2.5B tokens"
] | [
"TAGS\n#tensorboard #region-us \n",
"### Comparison of downsampling methods after 2.5B tokens"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **PushBlock**
This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingfa... | {"license": "apache-2.0", "library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-PushBlock"]} | unity/ML-Agents-PushBlock | null | [
"ml-agents",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-PushBlock",
"license:apache-2.0",
"region:us"
] | null | 2022-06-22T06:00:07+00:00 | [] | [] | TAGS
#ml-agents #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #license-apache-2.0 #region-us
|
# ppo Agent playing PushBlock
This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.
## Watch your Agent play
You can watch your agent playing directly in your browser:.
1. Go to URL
2. Step 1: Write your model_id: unity/ML-Agents-PushBlock
3. Step 2: Select you... | [
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You can watch your agent playing directly in your browser:. \n \n 1. Go to URL \n 2. Step 1: Write your model_id: unity/ML-Agents-PushBlock\n 3. Step 2... | [
"TAGS\n#ml-agents #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-PushBlock #license-apache-2.0 #region-us \n",
"# ppo Agent playing PushBlock\n This is a trained model of a ppo agent playing PushBlock using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You can ... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Walker**
This is a trained model of a **ppo** agent playing **Walker** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/... | {"license": "apache-2.0", "library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Walker"]} | unity/ML-Agents-Walker | null | [
"ml-agents",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Walker",
"license:apache-2.0",
"region:us"
] | null | 2022-06-22T06:07:20+00:00 | [] | [] | TAGS
#ml-agents #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Walker #license-apache-2.0 #region-us
|
# ppo Agent playing Walker
This is a trained model of a ppo agent playing Walker using the Unity ML-Agents Library.
## Watch your Agent play
You can watch your agent playing directly in your browser:.
1. Go to URL
2. Step 1: Write your model_id: unity/ML-Agents-Walker
3. Step 2: Select your *.nn or ... | [
"# ppo Agent playing Walker\n This is a trained model of a ppo agent playing Walker using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You can watch your agent playing directly in your browser:. \n \n 1. Go to URL\n 2. Step 1: Write your model_id: unity/ML-Agents-Walker\n 3. Step 2: Select y... | [
"TAGS\n#ml-agents #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Walker #license-apache-2.0 #region-us \n",
"# ppo Agent playing Walker\n This is a trained model of a ppo agent playing Walker using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You can watch you... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | Corianas/qrdqn-3Frame-SpaceInvadersNoFrameskip_1 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T06:11:59+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface... | {"license": "apache-2.0", "library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | unity/ML-Agents-Pyramids | null | [
"ml-agents",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"license:apache-2.0",
"region:us"
] | null | 2022-06-22T06:13:17+00:00 | [] | [] | TAGS
#ml-agents #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #license-apache-2.0 #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Watch your Agent play
You can watch your agent playing directly in your browser:.
1. Go to URL
2. Step 1: Write your model_id: unity/ML-Agents-Pyramids
3. Step 2: Select your *... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You can watch your agent playing directly in your browser:. \n \n 1. Go to URL \n 2. Step 1: Write your model_id: unity/ML-Agents-Pyramids\n 3. Step 2: S... | [
"TAGS\n#ml-agents #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #license-apache-2.0 #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Watch your Agent play\n You c... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | Corianas/qrdqn-3Frame-SpaceInvadersNoFrameskip_1.best | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T06:14:05+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# robingeibel/roBERTa-base-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/roBERTa-base-finetuned-big_patent](ht... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "robingeibel/roBERTa-base-finetuned-big_patent", "results": []}]} | robingeibel/roBERTa-base-finetuned-big_patent | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T07:13:56+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| robingeibel/roBERTa-base-finetuned-big\_patent
==============================================
This model is a fine-tuned version of robingeibel/roBERTa-base-finetuned-big\_patent on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1827
* Validation Loss: 1.0542
* Epoch: 0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #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\\_rate': {'class\... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | merve/text_image_dual_encoder | null | [
"keras",
"region:us"
] | null | 2022-06-22T07:17:04+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 Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
text-classification | transformers |
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large", "results": []}]} | Elron/deberta-v3-large-emotion | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T07:54:48+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-sentiment
==========================
This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet\_eval dataset.
Model description
-----------------
Test set results:
source:papers\_with\_code
Intended uses & limitations
---------------------------
Classifying attributes of... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* e... |
text-classification | transformers |
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large", "results": []}]} | Elron/deberta-v3-large-hate | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T07:55:07+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-sentiment
==========================
This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet\_eval dataset.
Model description
-----------------
Test set results:
source:papers\_with\_code
Intended uses & limitations
---------------------------
Classifying attributes of... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* e... |
text-classification | transformers |
# deberta-v3-large-irony
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large", "results": []}]} | Elron/deberta-v3-large-irony | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T07:55:47+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-irony
======================
This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet\_eval dataset.
Model description
-----------------
Test set results:
source:papers\_with\_code
Intended uses & limitations
---------------------------
Classifying attributes of interes... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-06\n* train\\_batch\\_size: 16\n* e... |
text-classification | transformers |
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large", "results": []}]} | Elron/deberta-v3-large-offensive | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T07:56:09+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-sentiment
==========================
This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet\_eval dataset.
Model description
-----------------
Test set results:
source:papers\_with\_code
Intended uses & limitations
---------------------------
Classifying attributes of... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* e... |
text-classification | transformers |
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large", "results": []}]} | Elron/deberta-v3-large-sentiment | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T07:56:37+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-sentiment
==========================
This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet\_eval dataset.
Model description
-----------------
Test set results:
source:papers\_with\_code
Intended uses & limitations
---------------------------
Classifying attributes of... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 16\n* e... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-large-squadshifts-amazon-qg`
This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://gi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-amazon-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T08:14:44+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-large-squadshifts-amazon-qg'
=====================================================
This model is fine-tuned version of lmqg/bart-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Language model: lmqg/bart-large-squad
* ... | [
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-large-squad\n* Language: en\n* Training data:... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | asnorkin/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T08:22:00+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | null | Test | {} | vikramai/test | null | [
"region:us"
] | null | 2022-06-22T08:24:23+00:00 | [] | [] | TAGS
#region-us
| Test | [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="asnorkin/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | asnorkin/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T08:35:11+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
image-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 260265
- CO2 Emissions (in grams): 8.217704896005591
## Validation Metrics
- Loss: 0.24580252170562744
- Accuracy: 0.914
- Macro F1: 0.912823674084623
- Micro F1: 0.914
- Weighted F1: 0.9128236740846232
- Macro Precision: 0.91356... | {"tags": "autotrain", "datasets": ["abhishek/autotrain-data-vision_528a5bd60a4b4b1080538a6ede3f23c7"], "co2_eq_emissions": 8.217704896005591} | abhishek/autotrain-vision_528a5bd60a4b4b1080538a6ede3f23c7-260265 | null | [
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-vision_528a5bd60a4b4b1080538a6ede3f23c7",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T08:50:00+00:00 | [] | [] | TAGS
#transformers #pytorch #swin #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_528a5bd60a4b4b1080538a6ede3f23c7 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 260265
- CO2 Emissions (in grams): 8.217704896005591
## Validation Metrics
- Loss: 0.24580252170562744
- Accuracy: 0.914
- Macro F1: 0.912823674084623
- Micro F1: 0.914
- Weighted F1: 0.9128236740846232
- Macro Precision: 0.91356... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 260265\n- CO2 Emissions (in grams): 8.217704896005591",
"## Validation Metrics\n\n- Loss: 0.24580252170562744\n- Accuracy: 0.914\n- Macro F1: 0.912823674084623\n- Micro F1: 0.914\n- Weighted F1: 0.9128236740846232\n- Macro... | [
"TAGS\n#transformers #pytorch #swin #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_528a5bd60a4b4b1080538a6ede3f23c7 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 26... |
image-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 264300
- CO2 Emissions (in grams): 2.050948967287266
## Validation Metrics
- Loss: 0.009216072037816048
- Accuracy: 0.9976190476190476
- Macro F1: 0.9973261861865685
- Micro F1: 0.9976190476190476
- Weighted F1: 0.997621154535828... | {"tags": "autotrain", "datasets": ["abhishek/autotrain-data-vision_652fee16113a4f07a2452e021a22a934", "sasha/dog-food"], "co2_eq_emissions": 2.050948967287266, "model-index": [{"name": "autotrain-dog-vs-food", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "sa... | abhishek/autotrain-dog-vs-food | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-vision_652fee16113a4f07a2452e021a22a934",
"dataset:sasha/dog-food",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:33:54+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_652fee16113a4f07a2452e021a22a934 #dataset-sasha/dog-food #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 264300
- CO2 Emissions (in grams): 2.050948967287266
## Validation Metrics
- Loss: 0.009216072037816048
- Accuracy: 0.9976190476190476
- Macro F1: 0.9973261861865685
- Micro F1: 0.9976190476190476
- Weighted F1: 0.997621154535828... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 264300\n- CO2 Emissions (in grams): 2.050948967287266",
"## Validation Metrics\n\n- Loss: 0.009216072037816048\n- Accuracy: 0.9976190476190476\n- Macro F1: 0.9973261861865685\n- Micro F1: 0.9976190476190476\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #vit #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_652fee16113a4f07a2452e021a22a934 #dataset-sasha/dog-food #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-subjqa-vanilla-books-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://gith... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-large-subjqa-vanilla-books-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:36:52+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-subjqa-vanilla-books-qg'
==================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: facebo... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/q... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-squadshifts-new_wiki-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://g... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-new_wiki-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:39:36+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-squadshifts-new\_wiki-qg'
=======================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squ... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric f... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: ... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squadshifts-vanilla-new_wiki-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`l... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-vanilla-new_wiki-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:39:37+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squadshifts-vanilla-new\_wiki-qg'
==========================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview
... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric fil... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lm... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squadshifts-vanilla-nyt-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](http... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-vanilla-nyt-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:41:18+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squadshifts-vanilla-nyt-qg'
====================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language model: ... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lm... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-squadshifts-nyt-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-nyt-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:41:23+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-squadshifts-nyt-qg'
=================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Language: en
... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: ... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-subjqa-vanilla-electronics-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](htt... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-base-subjqa-vanilla-electronics-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:41:38+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-subjqa-vanilla-electronics-qg'
=======================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Languag... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squadshifts-vanilla-reddit-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-vanilla-reddit-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:43:08+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squadshifts-vanilla-reddit-qg'
=======================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Languag... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lm... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-squadshifts-reddit-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://githu... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-reddit-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:43:16+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-squadshifts-reddit-qg'
====================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Lang... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: ... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-subjqa-vanilla-grocery-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://git... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-base-subjqa-vanilla-grocery-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:43:41+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-subjqa-vanilla-grocery-qg'
===================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: fac... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-subjqa-vanilla-books-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-base-subjqa-vanilla-books-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:44:48+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-subjqa-vanilla-books-qg'
=================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: facebook/... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nT... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-squadshifts-vanilla-amazon-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-vanilla-amazon-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:49:31+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-squadshifts-vanilla-amazon-qg'
=======================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Languag... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lm... |
text2text-generation | transformers |
# Model Card of `lmqg/bart-base-squadshifts-amazon-qg`
This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://githu... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-base-squadshifts-amazon-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:49:56+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'lmqg/bart-base-squadshifts-amazon-qg'
====================================================
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Language model: lmqg/bart-base-squad
* Lang... | [
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: lmqg/bart-base-squad\n* Language: en\n* Training data: ... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-subjqa-vanilla-movies-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://githu... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-base-subjqa-vanilla-movies-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:49:58+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-subjqa-vanilla-movies-qg'
==================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: facebo... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-subjqa-vanilla-restaurants-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](htt... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-base-subjqa-vanilla-restaurants-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:51:36+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-subjqa-vanilla-restaurants-qg'
=======================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Languag... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-base-subjqa-vanilla-tripadvisor-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](htt... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-base-subjqa-vanilla-tripadvisor-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:53:18+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-base-subjqa-vanilla-tripadvisor-qg'
=======================================================================
This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Languag... | [
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-base\n* Language: en\n* Training data: lmqg/qg... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squadshifts-vanilla-reddit-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lm... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-vanilla-reddit-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:56:15+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squadshifts-vanilla-reddit-qg'
========================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Lang... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: l... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-squadshifts-vanilla-amazon-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lm... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "<hl> Beyonce <hl> further expanded her acting career, starring as blues sing... | research-backup/bart-large-squadshifts-vanilla-amazon-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T09:58:34+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-squadshifts-vanilla-amazon-qg'
========================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Lang... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: l... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-subjqa-vanilla-electronics-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-large-subjqa-vanilla-electronics-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T10:02:11+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-subjqa-vanilla-electronics-qg'
========================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: electronics) via 'lmqg'.
### Overview
* Lang... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (electronics)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/q... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-subjqa-vanilla-grocery-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-large-subjqa-vanilla-grocery-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T10:20:30+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-subjqa-vanilla-grocery-qg'
====================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: grocery) via 'lmqg'.
### Overview
* Language model: ... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (grocery)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/q... |
null | null | # CogView2
## Model description
**CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers**
- [Paper](https://arxiv.org/abs/2204.14217)
- [GitHub Repo](https://github.com/THUDM/CogView2)
### Abstract
The development of the transformer-based text-to-image models are impeded by its slow gen... | {"license": "apache-2.0"} | THUDM/CogView2 | null | [
"arxiv:2204.14217",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-22T10:23:42+00:00 | [
"2204.14217"
] | [] | TAGS
#arxiv-2204.14217 #license-apache-2.0 #has_space #region-us
| # CogView2
## Model description
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
- Paper
- GitHub Repo
### Abstract
The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forwa... | [
"# CogView2",
"## Model description\n\nCogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers\n\n- Paper\n- GitHub Repo",
"### Abstract\n\nThe development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In th... | [
"TAGS\n#arxiv-2204.14217 #license-apache-2.0 #has_space #region-us \n",
"# CogView2",
"## Model description\n\nCogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers\n\n- Paper\n- GitHub Repo",
"### Abstract\n\nThe development of the transformer-based text-to-image models are imped... |
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... | Saraswati/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T10:28:21+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-subjqa-vanilla-movies-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://gi... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-large-subjqa-vanilla-movies-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T10:38:41+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-subjqa-vanilla-movies-qg'
===================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: movies) via 'lmqg'.
### Overview
* Language model: fac... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (movies)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/q... |
translation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1016534299
- CO2 Emissions (in grams): 0.07815966018818815
## Validation Metrics
- Loss: 0.9978321194648743
- SacreBLEU: 13.8459
- Gen len: 6.0588 | {"language": ["en", "es"], "tags": ["autotrain", "translation"], "datasets": ["Mizew/autotrain-data-avar"], "co2_eq_emissions": 0.07815966018818815} | Mizew/autotrain-avar-1016534299 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"translation",
"en",
"es",
"dataset:Mizew/autotrain-data-avar",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T10:55:38+00:00 | [] | [
"en",
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #translation #en #es #dataset-Mizew/autotrain-data-avar #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1016534299
- CO2 Emissions (in grams): 0.07815966018818815
## Validation Metrics
- Loss: 0.9978321194648743
- SacreBLEU: 13.8459
- Gen len: 6.0588 | [
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1016534299\n- CO2 Emissions (in grams): 0.07815966018818815",
"## Validation Metrics\n\n- Loss: 0.9978321194648743\n- SacreBLEU: 13.8459\n- Gen len: 6.0588"
] | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 10165342... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-subjqa-vanilla-restaurants-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-large-subjqa-vanilla-restaurants-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T10:56:52+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-subjqa-vanilla-restaurants-qg'
========================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: restaurants) via 'lmqg'.
### Overview
* Lang... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (restaurants)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/q... |
image-classification | transformers |
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | Matthijs/mobilenet_v1_1.0_224 | null | [
"transformers",
"pytorch",
"mobilenet_v1",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1704.04861",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T11:05:41+00:00 | [
"1704.04861"
] | [] | TAGS
#transformers #pytorch #mobilenet_v1 #image-classification #vision #dataset-imagenet-1k #arxiv-1704.04861 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository.
Disclaimer: The team releasing MobileNet V1 did not write a model card fo... | [
"# MobileNet V1\n\nMobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository.\n\nDisclaimer: The team releasing MobileNet V1 did not write a model... | [
"TAGS\n#transformers #pytorch #mobilenet_v1 #image-classification #vision #dataset-imagenet-1k #arxiv-1704.04861 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# MobileNet V1\n\nMobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in MobileNets: Effic... |
image-classification | transformers |
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | Matthijs/mobilenet_v1_0.75_192 | null | [
"transformers",
"pytorch",
"mobilenet_v1",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1704.04861",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T11:07:44+00:00 | [
"1704.04861"
] | [] | TAGS
#transformers #pytorch #mobilenet_v1 #image-classification #vision #dataset-imagenet-1k #arxiv-1704.04861 #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository.
Disclaimer: The team releasing MobileNet V1 did not write a model card fo... | [
"# MobileNet V1\n\nMobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Howard et al, and first released in this repository.\n\nDisclaimer: The team releasing MobileNet V1 did not write a model... | [
"TAGS\n#transformers #pytorch #mobilenet_v1 #image-classification #vision #dataset-imagenet-1k #arxiv-1704.04861 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# MobileNet V1\n\nMobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in MobileNets: Effic... |
text2text-generation | transformers |
# Model Card of `research-backup/bart-large-subjqa-vanilla-tripadvisor-qg`
This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/bart-large-subjqa-vanilla-tripadvisor-qg | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T11:17:02+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Model Card of 'research-backup/bart-large-subjqa-vanilla-tripadvisor-qg'
========================================================================
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: tripadvisor) via 'lmqg'.
### Overview
* Lang... | [
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (tripadvisor)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Overview\n\n\n* Language model: facebook/bart-large\n* Language: en\n* Training data: lmqg/q... |
image-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 300303
- CO2 Emissions (in grams): 32.869648157119876
## Validation Metrics
- Loss: 0.05070499703288078
- Accuracy: 0.9834
- Macro F1: 0.9834026834840477
- Micro F1: 0.9834
- Weighted F1: 0.9834026834840479
- Macro Precision: 0.9... | {"tags": "autotrain", "datasets": ["abhishek/autotrain-data-vision_79ca848474e24ad3a520c09e36452e85", "cifar10"], "co2_eq_emissions": 32.869648157119876, "model-index": [{"name": "autotrain_cifar10_vit_base", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cif... | abhishek/autotrain_cifar10_vit_base | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-vision_79ca848474e24ad3a520c09e36452e85",
"dataset:cifar10",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T11:21:22+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_79ca848474e24ad3a520c09e36452e85 #dataset-cifar10 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 300303
- CO2 Emissions (in grams): 32.869648157119876
## Validation Metrics
- Loss: 0.05070499703288078
- Accuracy: 0.9834
- Macro F1: 0.9834026834840477
- Micro F1: 0.9834
- Weighted F1: 0.9834026834840479
- Macro Precision: 0.9... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 300303\n- CO2 Emissions (in grams): 32.869648157119876",
"## Validation Metrics\n\n- Loss: 0.05070499703288078\n- Accuracy: 0.9834\n- Macro F1: 0.9834026834840477\n- Micro F1: 0.9834\n- Weighted F1: 0.9834026834840479\n- M... | [
"TAGS\n#transformers #pytorch #vit #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_79ca848474e24ad3a520c09e36452e85 #dataset-cifar10 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class C... |
translation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1018434345
- CO2 Emissions (in grams): 19.740487511182447
## Validation Metrics
- Loss: 0.9978321194648743
- SacreBLEU: 13.8459
- Gen len: 6.0588
## Description
This is a model for the Pannonian Rusyn language, Albeit the data i trained it on... | {"language": ["en", "es"], "tags": ["autotrain", "translation"], "datasets": ["Mizew/autotrain-data-rusyn2"], "co2_eq_emissions": 19.740487511182447} | Mizew/EN-RSK | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"translation",
"en",
"es",
"dataset:Mizew/autotrain-data-rusyn2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T11:39:17+00:00 | [] | [
"en",
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #translation #en #es #dataset-Mizew/autotrain-data-rusyn2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1018434345
- CO2 Emissions (in grams): 19.740487511182447
## Validation Metrics
- Loss: 0.9978321194648743
- SacreBLEU: 13.8459
- Gen len: 6.0588
## Description
This is a model for the Pannonian Rusyn language, Albeit the data i trained it on... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1018434345\n- CO2 Emissions (in grams): 19.740487511182447",
"## Validation Metrics\n\n- Loss: 0.9978321194648743\n- SacreBLEU: 13.8459\n- Gen len: 6.0588",
"## Description\n\nThis is a model for the Pannonian Rusyn language, Albeit th... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #translation #en #es #dataset-Mizew/autotrain-data-rusyn2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 101843... |
token-classification | transformers |
# Legal act Extraction Model
With growing legal complexity keeping track of changes in interconnectivity and hierarchical structure of the legislation is a challenging task. Entity extraction technique (also known as token classification) facilitates document analysis by assigning a label to each word in a text.
A ... | {"language": "en", "license": "mit", "metrics": ["seqeval"], "widget": [{"text": "When Member States adopt those measures, they shall contain a reference to this Directive or be accompanied by such reference on the occasion of their official publication. They shall also include a statement that references in existing l... | Lexemo/roberta_large_legal_act_extraction | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T12:53:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Legal act Extraction Model
==========================
With growing legal complexity keeping track of changes in interconnectivity and hierarchical structure of the legislation is a challenging task. Entity extraction technique (also known as token classification) facilitates document analysis by assigning a label to ... | [
"### Limitations\n\n\nThis legal-act extraction model is very domain-specific and will perform well on legal texts. It's not recommended to use this model for other domains, but you are free to test it out.\nIt was intended for English documents only.",
"### How To Use\n\n\nFine-tuning hyper-parameters\n---------... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Limitations\n\n\nThis legal-act extraction model is very domain-specific and will perform well on legal texts. It's not recommended to use this model for other domains, bu... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="fgmckee/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | fgmckee/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T13:20:44+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
null | null | BREAKING BAD, but with a plot twist...
W.W - "JESSE!!! WE NEED TO COOK!"
Jesse - "Mista White i cant, i have "plans.."
W.W - "i watched Jane die"
Jesse - ":0"
W.W - "I also killed Gus Fring, so be happy. Jane could've killed you and make your life worse than it is so i helped you"
Jesse - "Oh, i didn't realize M... | {} | LukeTheAI/jesse | null | [
"region:us"
] | null | 2022-06-22T13:32:04+00:00 | [] | [] | TAGS
#region-us
| BREAKING BAD, but with a plot twist...
W.W - "JESSE!!! WE NEED TO COOK!"
Jesse - "Mista White i cant, i have "plans.."
W.W - "i watched Jane die"
Jesse - ":0"
W.W - "I also killed Gus Fring, so be happy. Jane could've killed you and make your life worse than it is so i helped you"
Jesse - "Oh, i didn't realize M... | [] | [
"TAGS\n#region-us \n"
] |
null | null |
Mirror of OpenFold parameters as provided in https://github.com/aqlaboratory/openfold. Stopgap solution as the original download link was down. Updated based on the s3 bucket parameter update. All rights to the authors.
OpenFold model parameters, v. 06_22.
# Training details:
Trained using OpenFold on 44 A100s usi... | {"license": "cc-by-4.0"} | nz/OpenFold | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-22T13:32:13+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
|
Mirror of OpenFold parameters as provided in URL Stopgap solution as the original download link was down. Updated based on the s3 bucket parameter update. All rights to the authors.
OpenFold model parameters, v. 06_22.
# Training details:
Trained using OpenFold on 44 A100s using the training schedule from Table 4 ... | [
"# Training details:\n\nTrained using OpenFold on 44 A100s using the training schedule from Table 4 in\nthe AlphaFold supplement. AlphaFold was used as the pre-distillation model. \nTraining data is hosted publicly in the \"OpenFold Training Data\" RODA repository.\n\nTo improve model diversity, we forked training ... | [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# Training details:\n\nTrained using OpenFold on 44 A100s using the training schedule from Table 4 in\nthe AlphaFold supplement. AlphaFold was used as the pre-distillation model. \nTraining data is hosted publicly in the \"OpenFold Training Data\" RODA repository.\n\nTo ... |
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. -->
# bio_bert_ft
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the N... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "bio_bert_ft", "results": []}]} | ericntay/bio_bert_ft | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T13:35:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bio\_bert\_ft
=============
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0747
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="fgmckee/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/... | fgmckee/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T13:37:39+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | transformers |
This Repository includes the files required to run the `STEM Annotation` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. | {"license": "mit"} | orkg/orkgnlp-stem | null | [
"transformers",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T13:49:50+00:00 | [] | [] | TAGS
#transformers #license-mit #endpoints_compatible #region-us
|
This Repository includes the files required to run the 'STEM Annotation' ORKG-NLP service.
Please check this article for more details about the service. | [] | [
"TAGS\n#transformers #license-mit #endpoints_compatible #region-us \n"
] |
image-classification | pytorch |
# CNN | {"library_name": "pytorch", "tags": ["image-classification"]} | luantber/k_cnn_cifar10 | null | [
"pytorch",
"image-classification",
"custom_code",
"region:us"
] | null | 2022-06-22T13:50:28+00:00 | [] | [] | TAGS
#pytorch #image-classification #custom_code #region-us
|
# CNN | [
"# CNN"
] | [
"TAGS\n#pytorch #image-classification #custom_code #region-us \n",
"# CNN"
] |
translation | transformers | # How to run the model
```python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
model = M2M100ForConditionalGeneration.from_pretrained("transZ/M2M_Vi_Ba")
tokenizer = M2M100Tokenizer.from_pretrained("transZ/M2M_Vi_Ba")
tokenizer.src_lang = "vi"
vi_text = "Hôm nay ba đi chợ."
encoded_vi = toke... | {"language": ["vi", "ba"], "tags": ["translation"], "datasets": ["custom dataset"], "metrics": ["bleu", "sacrebleu"]} | transZ/M2M_Vi_Ba | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"translation",
"vi",
"ba",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T14:26:10+00:00 | [] | [
"vi",
"ba"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #translation #vi #ba #autotrain_compatible #endpoints_compatible #region-us
| # How to run the model
| [
"# How to run the model"
] | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #translation #vi #ba #autotrain_compatible #endpoints_compatible #region-us \n",
"# How to run the model"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mmazuecos/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | mmazuecos/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T14:57:01+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
image-classification | transformers |
# where_am_I_hospital-balcony-hallway-airport-coffee-house
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [git... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | mayoughi/where_am_I_hospital-balcony-hallway-airport-coffee-house | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T15:00:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# where_am_I_hospital-balcony-hallway-airport-coffee-house
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### airport
!airport
#### balcony
!balcony
#### coffee house ind... | [
"# where_am_I_hospital-balcony-hallway-airport-coffee-house\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### airport\n\n!airport",
"#### balcony\n\n!balco... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# where_am_I_hospital-balcony-hallway-airport-coffee-house\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mmazuecos/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | mmazuecos/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T15:01:48+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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... | Andyrasika/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-22T15:04:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 0.1383
* F1: 0.8589
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 #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
null | null |
# Graphcore/convnext-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphc... | {"license": "apache-2.0"} | Graphcore/convnext-base-ipu | null | [
"optimum_graphcore",
"arxiv:2201.03545",
"license:apache-2.0",
"region:us"
] | null | 2022-06-22T15:30:43+00:00 | [
"2201.03545"
] | [] | TAGS
#optimum_graphcore #arxiv-2201.03545 #license-apache-2.0 #region-us
|
# Graphcore/convnext-base-ipu
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphc... | [
"# Graphcore/convnext-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on ... | [
"TAGS\n#optimum_graphcore #arxiv-2201.03545 #license-apache-2.0 #region-us \n",
"# Graphcore/convnext-base-ipu\n\nOptimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of perf... |
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_RCADE_fine_tuned_sentiment_covid_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta_RCADE_fine_tuned_sentiment_covid_news", "results": []}]} | RogerKam/roberta_RCADE_fine_tuned_sentiment_covid_news | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T15:58:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta_RCADE_fine_tuned_sentiment_covid_news
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1662
- Accuracy: 0.9700
- F1 Score: 0.9700
## Model description
More information needed
## Intended uses & limitations
More i... | [
"# roberta_RCADE_fine_tuned_sentiment_covid_news\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1662\n- Accuracy: 0.9700\n- F1 Score: 0.9700",
"## Model description\n\nMore information needed",
"## Intended uses & l... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta_RCADE_fine_tuned_sentiment_covid_news\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the fol... |
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... | jamesmarcel/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 | 2022-06-22T16:03:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1372
* F1: 0.8621
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# xlm-roberta-base_single_finetuned_on_cedr_augmented
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "xlm-roberta-base_single_finetuned_on_cedr_augmented", "results": []}]} | mmillet/xlm-roberta-base_single_finetuned_on_cedr_augmented | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T16:23:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base\_single\_finetuned\_on\_cedr\_augmented
========================================================
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: 0.4650
* Accuracy: 0.8820
* F1: 0.8814
* Precision: 0.8871
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 0.0001\n* train\\_batch\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wiselinjayajos/squad_modified_for_t5_qg"], "widget": [{"text": "generate question: Python is developed by Guido Van Rossum and released in 1991.</s>"}], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]} | wiselinjayajos/t5-end2end-questions-generation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wiselinjayajos/squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T16:26:44+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-wiselinjayajos/squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| t5-end2end-questions-generation
===============================
This model is a fine-tuned version of t5-base on the squad\_modified\_for\_t5\_qg dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5789
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-wiselinjayajos/squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters we... |
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. -->
# newsclassifier
This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "newsclassifier", "results": []}]} | sherover125/newsclassifier | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T16:28:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| newsclassifier
==============
This model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1405
* Matthews Correlation: 0.9731
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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 #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: 3e-05\n* train\\_batch\\... |
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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model descrip... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds", "results": []}]} | atendstowards0/codeparrot-ds | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T16:45:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# codeparrot-ds
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hype... | [
"# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyper... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore info... |
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. -->
# testing0
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "testing0", "results": []}]} | atendstowards0/testing0 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T17:12:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# testing0
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperpara... | [
"# testing0\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparam... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# testing0\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore informati... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | sinhprous/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T17:19:11+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. -->
# xlmroberta-2nd-finetune-epru
This model is a fine-tuned version of [mmillet/xlm-roberta-base_single_finetuned_on_cedr_augmented]... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "xlmroberta-2nd-finetune-epru", "results": []}]} | mmillet/xlmroberta-2nd-finetune-epru | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T17:38:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlmroberta-2nd-finetune-epru
============================
This model is a fine-tuned version of mmillet/xlm-roberta-base\_single\_finetuned\_on\_cedr\_augmented on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3666
* Accuracy: 0.9325
* F1: 0.9329
* Precision: 0.9352
* Recall... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #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: 0.0001\n* train\\_batch\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dk-crazydiv/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | dk-crazydiv/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T18:05:21+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dk-crazydiv/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | dk-crazydiv/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T18:08:04+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **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... | micheljperez/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T18:08:38+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. -->
# 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"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | bousejin/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T18:14:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #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: 0.2202
* Accuracy: 0.925
* F1: 0.9252
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 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1021934687
- CO2 Emissions (in grams): 16.368556687663705
## Validation Metrics
- Loss: 0.15712647140026093
- Accuracy: 0.9503340757238308
- Precision: 0.9515767251616308
- Recall: 0.9598083577322332
- AUC: 0.9857179850355002
- F1: 0.... | {"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-bert_wikipedia_sst2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 16.368556687663705} | deepesh0x/bert_wikipedia_sst2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:deepesh0x/autotrain-data-bert_wikipedia_sst2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T20:18:44+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-bert_wikipedia_sst2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1021934687
- CO2 Emissions (in grams): 16.368556687663705
## Validation Metrics
- Loss: 0.15712647140026093
- Accuracy: 0.9503340757238308
- Precision: 0.9515767251616308
- Recall: 0.9598083577322332
- AUC: 0.9857179850355002
- F1: 0.... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1021934687\n- CO2 Emissions (in grams): 16.368556687663705",
"## Validation Metrics\n\n- Loss: 0.15712647140026093\n- Accuracy: 0.9503340757238308\n- Precision: 0.9515767251616308\n- Recall: 0.9598083577322332\n- AUC: 0.9857179... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-bert_wikipedia_sst2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1021934687\n- CO2 Emis... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Dugerij/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attr... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Dugerij/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T20:37:13+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Dugerij/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | Dugerij/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T20:43:47+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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