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values | library_name stringclasses 198
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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="JMillan/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 +/... | JMillan/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-22T21:35:46+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 |
### German Intesifiers Tagging | {"language": ["de"], "license": "cc-by-4.0", "tags": ["token classificaition"]} | TariqYousef/german-intensifiers-tagging | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"token classificaition",
"de",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T21:36:28+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #token-classification #token classificaition #de #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
### German Intesifiers Tagging | [
"### German Intesifiers Tagging"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #token classificaition #de #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### German Intesifiers Tagging"
] |
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... | Dugerij/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-22T21:43:10+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-generation | transformers |
# Dora DialoGPT Model | {"tags": ["conversational"]} | Bman/DialoGPT-medium-dora | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T22:28:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dora DialoGPT Model | [
"# Dora DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dora DialoGPT Model"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1536036266818555907/0Mq-... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/glitchfur-jdthe65th-zenitho_o/1655941045991/predictions.png", "widget": [{"text": "My dream is"}]} | JdThe65th/GPT2-Glitchfur-Zenith-JD | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-22T22:45:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Glitch & The 65th JD & zenith
@glitchfur-jdthe65th-zenitho\_o
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1022334717
- CO2 Emissions (in grams): 36.77525840351019
## Validation Metrics
- Loss: 2.2217929363250732
- Rouge1: 24.2547
- Rouge2: 10.0483
- RougeL: 19.7
- RougeLsum: 20.0966
- Gen Len: 19.6899
## Usage
You can use cURL to access this mo... | {"language": "unk", "tags": "autotrain", "datasets": ["Chemsseddine/autotrain-data-Mbarthez"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 36.77525840351019} | Chemsseddine/mBarthezMlsum | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain",
"unk",
"dataset:Chemsseddine/autotrain-data-Mbarthez",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-22T23:57:30+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain #unk #dataset-Chemsseddine/autotrain-data-Mbarthez #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1022334717
- CO2 Emissions (in grams): 36.77525840351019
## Validation Metrics
- Loss: 2.2217929363250732
- Rouge1: 24.2547
- Rouge2: 10.0483
- RougeL: 19.7
- RougeLsum: 20.0966
- Gen Len: 19.6899
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1022334717\n- CO2 Emissions (in grams): 36.77525840351019",
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"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain #unk #dataset-Chemsseddine/autotrain-data-Mbarthez #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1022334717\n- CO2 Emissions (in gram... |
null | null | This model is really only supposed to be for my [patreon patrons](https://www.patreon.com/kaliyuga_ai). I ask that, unless you *truly* can't afford to pay $5 to access this model, you not use it without being a patron. Regardless, you must give attribution if you use this model in any product/app/game, etc
| {"license": "cc-by-4.0"} | KaliYuga/spritesheetdiffusion | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-23T00:10:50+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
| This model is really only supposed to be for my patreon patrons. I ask that, unless you *truly* can't afford to pay $5 to access this model, you not use it without being a patron. Regardless, you must give attribution if you use this model in any product/app/game, etc
| [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | sonalily/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T00:12:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6429
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln52")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln52")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln52 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T00:38:07+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rule_learning_margin_1mm_spanpred_attention
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingfa... | {"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_1mm_spanpred_attention", "results": []}]} | enoriega/rule_learning_margin_1mm_spanpred_attention | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T01:15:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us
| rule\_learning\_margin\_1mm\_spanpred\_attention
================================================
This model is a fine-tuned version of enoriega/rule\_softmatching on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3237
* Margin Accuracy: 0.8518
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377
## Validation Metrics
- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580... | {"language": "en", "tags": "autotrain", "datasets": ["justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 19.2150872382377} | justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634731 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T01:16:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377
## Validation Metrics
- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1022634731\n- CO2 Emissions (in grams): 19.2150872382377",
"## Validation Metrics\n\n- Loss: 0.44044896960258484\n- Accuracy: 0.9149108589951378\n- Macro F1: 0.9112823337353622\n- Micro F1: 0.9149108589951378\n- Weighted F... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Mode... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634735
- CO2 Emissions (in grams): 16.816741650923202
## Validation Metrics
- Loss: 0.4373569190502167
- Accuracy: 0.9027552674230146
- Macro F1: 0.8938134766263609
- Micro F1: 0.9027552674230146
- Weighted F1: 0.902365385255... | {"language": "en", "tags": "autotrain", "datasets": ["justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 16.816741650923202} | justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T01:16:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634735
- CO2 Emissions (in grams): 16.816741650923202
## Validation Metrics
- Loss: 0.4373569190502167
- Accuracy: 0.9027552674230146
- Macro F1: 0.8938134766263609
- Micro F1: 0.9027552674230146
- Weighted F1: 0.902365385255... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1022634735\n- CO2 Emissions (in grams): 16.816741650923202",
"## Validation Metrics\n\n- Loss: 0.4373569190502167\n- Accuracy: 0.9027552674230146\n- Macro F1: 0.8938134766263609\n- Micro F1: 0.9027552674230146\n- Weighted ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model I... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634736
- CO2 Emissions (in grams): 0.07599569505565718
## Validation Metrics
- Loss: 0.4418122172355652
- Accuracy: 0.8914100486223663
- Macro F1: 0.8728966156620259
- Micro F1: 0.8914100486223663
- Weighted F1: 0.89098050605... | {"language": "en", "tags": "autotrain", "datasets": ["justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07599569505565718} | justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634736 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T01:16:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1022634736
- CO2 Emissions (in grams): 0.07599569505565718
## Validation Metrics
- Loss: 0.4418122172355652
- Accuracy: 0.8914100486223663
- Macro F1: 0.8728966156620259
- Micro F1: 0.8914100486223663
- Weighted F1: 0.89098050605... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1022634736\n- CO2 Emissions (in grams): 0.07599569505565718",
"## Validation Metrics\n\n- Loss: 0.4418122172355652\n- Accuracy: 0.8914100486223663\n- Macro F1: 0.8728966156620259\n- Micro F1: 0.8914100486223663\n- Weighted... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model I... |
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_chord_ft_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://h... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_chord_ft_wav2vec2-large-xlsr-53", "results": []}]} | gary109/ai-light-dance_chord_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 | 2022-06-23T01:47:00+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\_chord\_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-CHORD2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8722
* Wer: 0.9590
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 3e-05\n* ... |
automatic-speech-recognition | transformers | This project is meant to fine-tune the facebook/wav2vec2 speech-to-text library using my voice specifically for my own speech to text purposes. | {} | sharpcoder/wav2vec2_bjorn | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T01:53:37+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| This project is meant to fine-tune the facebook/wav2vec2 speech-to-text library using my voice specifically for my own speech to text purposes. | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Nothing, just from tutorial | {} | winson/bert-finetuned-ner-accelerate | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T01:54:25+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Nothing, just from tutorial | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | null |
# RWKV-2 430M
## Model Description
RWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it.
ctx_len = 768 n_layer = 24 n_embd = 1024
Final checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["The Pile"]} | BlinkDL/rwkv-2-pile-430m | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-23T02:09:51+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us
|
# RWKV-2 430M
## Model Description
RWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See URL for details.
At this moment you have to use my Github code (URL to run it.
ctx_len = 768 n_layer = 24 n_embd = 1024
Final checkpoint: URL : Trained on the Pile for 331B tokens.
* Pile loss 2.349
* LAMB... | [
"# RWKV-2 430M",
"## Model Description\n\nRWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See URL for details.\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = 768 n_layer = 24 n_embd = 1024\n\nFinal checkpoint: URL : Trained on the Pile for 331B tokens.\n* Pile ... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us \n",
"# RWKV-2 430M",
"## Model Description\n\nRWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See URL for details.\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = ... |
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. -->
# 4L_weight_decay
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation s... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "4L_weight_decay", "results": []}]} | kktoto/4L_weight_decay | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T02:17:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| 4L\_weight\_decay
=================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1312
* Precision: 0.7006
* Recall: 0.6863
* F1: 0.6934
* Accuracy: 0.9524
Model description
-----------------
More information needed
Intended uses ... | [
"### 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\\... |
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. -->
# scibert-finetuned-DAGPap22
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allen... | {"tags": ["text-classification", "generated_from_trainer"], "model-index": [{"name": "scibert-finetuned-DAGPap22", "results": []}]} | domenicrosati/scibert-finetuned-DAGPap22 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T02:23:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# scibert-finetuned-DAGPap22
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# scibert-finetuned-DAGPap22\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# scibert-finetuned-DAGPap22\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_uncased on an unknown dataset.",
"## Model description\n\nMore... |
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_singing2_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", "/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53", "results": []}]} | gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T02:43:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ai-light-dance\_singing2\_ft\_wav2vec2-large-xlsr-53
====================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the /WORKSPACE/ASANTE/AI-LIGHT-DANCE\_DATASETS/AI\_LIGHT\_DANCE.PY - ONSET-SINGING2 dataset.
It achieves the following results on the evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\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 #/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during traini... |
null | null | Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/428
# Pre-trained Transducer-Stateless5 models for the TAL_CSASR dataset with icefall.
The model was trained on the far data of [TAL_CSASR](https://ai.100tal.com/dataset) with the scripts in [icefall](https://github.com/k2-f... | {} | luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5 | null | [
"tensorboard",
"has_space",
"region:us"
] | null | 2022-06-23T03:14:43+00:00 | [] | [] | TAGS
#tensorboard #has_space #region-us
| Note: This recipe is trained with the codes from this PR URL
Pre-trained Transducer-Stateless5 models for the TAL\_CSASR dataset with icefall.
=================================================================================
The model was trained on the far data of TAL\_CSASR with the scripts in icefall based on th... | [] | [
"TAGS\n#tensorboard #has_space #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="Nyavol/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.42 +/... | Nyavol/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-23T03:37:06+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 | Fine Tune MNIST dataset on the ViT TrOCR model
accuracy = 0.99525
ref:
http://yann.lecun.com/exdb/mnist/
https://github.com/microsoft/unilm/tree/master/trocr | {} | aico/TrOCR-MNIST | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-23T05:47:08+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #has_space #region-us
| Fine Tune MNIST dataset on the ViT TrOCR model
accuracy = 0.99525
ref:
URL
URL | [] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #has_space #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | sun1638650145/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-23T06:00:40+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bn-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_9_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bn-colab", "results": []}]} | rhr99/wav2vec2-large-xls-r-300m-bn-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_9_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T06:45:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-bn-colab
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_9\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4662
* Wer: 0.9861
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_9_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\... |
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-v1
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T06:53:29+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-v1
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the eva... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-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: 4e-05\n* ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1024534822
- CO2 Emissions (in grams): 2.288443953210163
## Validation Metrics
- Loss: 0.5510443449020386
- Accuracy: 0.7619047619047619
- Precision: 0.6761363636363636
- Recall: 0.7345679012345679
- AUC: 0.7936883912336109
- F1: 0.70... | {"language": "en", "tags": "autotrain", "datasets": ["cjbarrie/autotrain-data-traintest-sentiment-split"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.288443953210163} | cjbarrie/autotrain-atc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:cjbarrie/autotrain-data-traintest-sentiment-split",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T06:59:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-cjbarrie/autotrain-data-traintest-sentiment-split #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1024534822
- CO2 Emissions (in grams): 2.288443953210163
## Validation Metrics
- Loss: 0.5510443449020386
- Accuracy: 0.7619047619047619
- Precision: 0.6761363636363636
- Recall: 0.7345679012345679
- AUC: 0.7936883912336109
- F1: 0.70... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1024534822\n- CO2 Emissions (in grams): 2.288443953210163",
"## Validation Metrics\n\n- Loss: 0.5510443449020386\n- Accuracy: 0.7619047619047619\n- Precision: 0.6761363636363636\n- Recall: 0.7345679012345679\n- AUC: 0.793688391... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-cjbarrie/autotrain-data-traintest-sentiment-split #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1024534822\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177
## Validation Metrics
- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6... | {"language": "en", "tags": "autotrain", "datasets": ["cjbarrie/autotrain-data-traintest-sentiment-split"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.1566482249518177} | cjbarrie/autotrain-atc2 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:cjbarrie/autotrain-data-traintest-sentiment-split",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T06:59:46+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-cjbarrie/autotrain-data-traintest-sentiment-split #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177
## Validation Metrics
- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6... | [
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"## Validation Metrics\n\n- Loss: 0.5167999267578125\n- Accuracy: 0.7523809523809524\n- Precision: 0.7377049180327869\n- Recall: 0.5555555555555556\n- AUC: 0.81425256... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 1024534825\n- C... |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework fo... | {"library_name": "stable-baselines3", "tags": ["BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BeamRiderNoFrameskip-... | Corianas/qrdqn-3frame_BeamRiderNoFrameskip-v4 | null | [
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-23T07:15:03+00:00 | [] | [] | TAGS
#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing BeamRiderNoFrameskip-v4
This is a trained model of a QRDQN agent playing BeamRiderNoFrameskip-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 inc... | [
"# QRDQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained... | [
"TAGS\n#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe... |
null | null |
# YaLM 100B
https://github.com/yandex/YaLM-100B
**YaLM 100B** is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.
The model leverages 100 billion parameters. It took 65 days to train the model on a cluster of 800 A100 graphics... | {"language": ["en", "ru"], "license": "apache-2.0", "tags": ["gpt", "NLG"]} | yandex/yalm-100b | null | [
"tensorboard",
"gpt",
"NLG",
"en",
"ru",
"license:apache-2.0",
"region:us"
] | null | 2022-06-23T07:19:11+00:00 | [] | [
"en",
"ru"
] | TAGS
#tensorboard #gpt #NLG #en #ru #license-apache-2.0 #region-us
|
# YaLM 100B
URL
YaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.
The model leverages 100 billion parameters. It took 65 days to train the model on a cluster of 800 A100 graphics cards and 1.7 TB of online texts, b... | [
"# YaLM 100B\n\nURL\n\nYaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.\n\nThe model leverages 100 billion parameters. It took 65 days to train the model on a cluster of 800 A100 graphics cards and 1.7 TB of onlin... | [
"TAGS\n#tensorboard #gpt #NLG #en #ru #license-apache-2.0 #region-us \n",
"# YaLM 100B\n\nURL\n\nYaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.\n\nThe model leverages 100 billion parameters. It took 65 days to... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | jgriffi/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T08:29:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4841
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
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... | dfomin/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-23T08:32:13+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
See https://github.com/k2-fsa/icefall/pull/439
| {} | ezerhouni/icefall-librispeech-rnn-lm | null | [
"tensorboard",
"onnx",
"region:us"
] | null | 2022-06-23T09:06:38+00:00 | [] | [] | TAGS
#tensorboard #onnx #region-us
| # Introduction
See URL
| [
"# Introduction\n\nSee URL"
] | [
"TAGS\n#tensorboard #onnx #region-us \n",
"# Introduction\n\nSee URL"
] |
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... | KayKozaronek/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 | 2022-06-23T09:13:29+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-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | iaanimashaun/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T09:57:06+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6895
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-erichmariaremarque
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknow... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-erichmariaremarque", "results": []}]} | mikegarts/distilgpt2-erichmariaremarque | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T10:40:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# distilgpt2-erichmariaremarque
This model is a fine-tuned version of distilgpt2 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 hyperparamete... | [
"# distilgpt2-erichmariaremarque\n\nThis model is a fine-tuned version of distilgpt2 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",... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# distilgpt2-erichmariaremarque\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset... |
text-generation | null |
# RWKV-3 1.5B
## Model Description
RWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
RWKV-4 1.5B is out: https://huggingface.co/BlinkDL/rwkv-4-pile-1b5
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile)... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["The Pile"]} | BlinkDL/rwkv-3-pile-1b5 | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-06-23T10:44:36+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us
|
# RWKV-3 1.5B
## Model Description
RWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.
RWKV-4 1.5B is out: URL
At this moment you have to use my Github code (URL to run it.
ctx_len = 896
n_layer = 24
n_embd = 2048
Preview checkpoint: URL : Trained on the Pile for 127B token... | [
"# RWKV-3 1.5B",
"## Model Description\n\nRWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.\n\nRWKV-4 1.5B is out: URL\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = 896\nn_layer = 24\nn_embd = 2048\n\nPreview checkpoint: URL : Trained on the... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us \n",
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"## Model Description\n\nRWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.\n\nRWKV-4 1.5B is out: URL\n\nAt this moment you have to use my Github code (U... |
image-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 7024732
- CO2 Emissions (in grams): 0.2438639401641305
## Validation Metrics
- Loss: 0.16775867342948914
- Accuracy: 0.9473333333333334
- Macro F1: 0.9473921270228505
- Micro F1: 0.9473333333333334
- Weighted F1: 0.94739212702285... | {"tags": "autotrain", "datasets": ["abhishek/autotrain-data-vision_877913e77fb94b7abd4dafc5ebf830b0", "fashion_mnist"], "co2_eq_emissions": 0.2438639401641305, "model-index": [{"name": "autotrain_fashion_mnist_vit_base", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {... | abhishek/autotrain_fashion_mnist_vit_base | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"dataset:abhishek/autotrain-data-vision_877913e77fb94b7abd4dafc5ebf830b0",
"dataset:fashion_mnist",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-23T11:59:26+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_877913e77fb94b7abd4dafc5ebf830b0 #dataset-fashion_mnist #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 7024732
- CO2 Emissions (in grams): 0.2438639401641305
## Validation Metrics
- Loss: 0.16775867342948914
- Accuracy: 0.9473333333333334
- Macro F1: 0.9473921270228505
- Micro F1: 0.9473333333333334
- Weighted F1: 0.94739212702285... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 7024732\n- CO2 Emissions (in grams): 0.2438639401641305",
"## Validation Metrics\n\n- Loss: 0.16775867342948914\n- Accuracy: 0.9473333333333334\n- Macro F1: 0.9473921270228505\n- Micro F1: 0.9473333333333334\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #vit #image-classification #autotrain #dataset-abhishek/autotrain-data-vision_877913e77fb94b7abd4dafc5ebf830b0 #dataset-fashion_mnist #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem ty... |
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... | joefarrington/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-23T12:10:22+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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | kidzy/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T12:17:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2240
* Accuracy: 0.9245
* F1: 0.9246
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- Rouge2: 34.1547
- RougeL: 54.4274
- RougeLsum: 54.408
- Gen Len: 23.178
## Usage
You can use cURL to access this m... | {"language": "unk", "tags": "autotrain", "datasets": ["hellennamulinda/autotrain-data-agric-eng-lug"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.04087910671538076} | hellennamulinda/agric-eng-lug | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T12:50:37+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- Rouge2: 34.1547
- RougeL: 54.4274
- RougeLsum: 54.408
- Gen Len: 23.178
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1026034854\n- CO2 Emissions (in grams): 0.04087910671538076",
"## Validation Metrics\n\n- Loss: 1.0871405601501465\n- Rouge1: 55.8225\n- Rouge2: 34.1547\n- RougeL: 54.4274\n- RougeLsum: 54.408\n- Gen Len: 23.178",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain #unk #dataset-hellennamulinda/autotrain-data-agric-eng-lug #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1026034854\n- CO2 Emissions (... |
image-classification | transformers |
# ONNX convert of ViT (base-sized model)
Conversion of [ViT-base](https://huggingface.co/google/vit-base-patch16-224), which has a classification head to perform **image classification**.
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 c... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k", "imagenet-21k"], "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/teapo... | optimum/vit-base-patch16-224 | null | [
"transformers",
"onnx",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T14:04:49+00:00 | [
"2010.11929",
"2006.03677"
] | [] | TAGS
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|
# ONNX convert of ViT (base-sized model)
Conversion of ViT-base, which has a classification head to perform image classification.
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet ... | [
"# ONNX convert of ViT (base-sized model)\n\nConversion of ViT-base, which has a classification head to perform image classification.",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned ... | [
"TAGS\n#transformers #onnx #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ONNX convert of ViT (base-sized model)\n\nConversion of ViT-base, which has a classificati... |
text-classification | transformers |
# M-CTC-T
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on ... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr", "common_voice"]} | cwkeam/m-ctc-t-large-sequence-lid | null | [
"transformers",
"pytorch",
"mctct",
"text-classification",
"speech",
"en",
"dataset:librispeech_asr",
"dataset:common_voice",
"arxiv:2111.00161",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T14:10:55+00:00 | [
"2111.00161"
] | [
"en"
] | TAGS
#transformers #pytorch #mctct #text-classification #speech #en #dataset-librispeech_asr #dataset-common_voice #arxiv-2111.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M-CTC-T
=======
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After traini... | [] | [
"TAGS\n#transformers #pytorch #mctct #text-classification #speech #en #dataset-librispeech_asr #dataset-common_voice #arxiv-2111.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# M-CTC-T
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on ... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr", "common_voice"]} | cwkeam/m-ctc-t-large-frame-lid | null | [
"transformers",
"pytorch",
"mctct",
"speech",
"en",
"dataset:librispeech_asr",
"dataset:common_voice",
"arxiv:2111.00161",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T14:11:10+00:00 | [
"2111.00161"
] | [
"en"
] | TAGS
#transformers #pytorch #mctct #speech #en #dataset-librispeech_asr #dataset-common_voice #arxiv-2111.00161 #license-apache-2.0 #endpoints_compatible #region-us
| M-CTC-T
=======
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After traini... | [] | [
"TAGS\n#transformers #pytorch #mctct #speech #en #dataset-librispeech_asr #dataset-common_voice #arxiv-2111.00161 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 1026434913
- CO2 Emissions (in grams): 7.300283563922049
## Validation Metrics
- Loss: 0.5467672348022461
- MSE: 0.5467672944068909
- MAE: 0.5851736068725586
- R2: 0.6883510493648173
- RMSE: 0.7394371628761292
- Explained Variance:... | {"language": "en", "tags": "autotrain", "datasets": ["404E/autotrain-data-formality"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.300283563922049} | 404E/autotrain-formality-1026434913 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:404E/autotrain-data-formality",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T14:15:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-404E/autotrain-data-formality #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 1026434913
- CO2 Emissions (in grams): 7.300283563922049
## Validation Metrics
- Loss: 0.5467672348022461
- MSE: 0.5467672944068909
- MAE: 0.5851736068725586
- R2: 0.6883510493648173
- RMSE: 0.7394371628761292
- Explained Variance:... | [
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"## Validation Metrics\n\n- Loss: 0.5467672348022461\n- MSE: 0.5467672944068909\n- MAE: 0.5851736068725586\n- R2: 0.6883510493648173\n- RMSE: 0.7394371628761292\n- ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-404E/autotrain-data-formality #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Single Column Regression\n- Model ID: 1026434913\n- CO2 Emissions (in gra... |
text-generation | transformers |
# Hermite DialoGPT Model | {"tags": ["conversational"]} | Hermite/DialoGPT-large-hermite3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T14:42:33+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Hermite DialoGPT Model | [
"# Hermite DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Hermite DialoGPT Model"
] |
text-generation | transformers |
# A model based on UberHaxorNova's Twitch chat
Trained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset.
## Dataset
The dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat messages into a simple l... | {"license": "mit"} | DingosGotMyBaby/uhn-twitch-chat | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T14:59:10+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# A model based on UberHaxorNova's Twitch chat
Trained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset.
## Dataset
The dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat messages into a simple l... | [
"# A model based on UberHaxorNova's Twitch chat \n\nTrained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset.",
"## Dataset\n\nThe dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat messages into ... | [
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"# A model based on UberHaxorNova's Twitch chat \n\nTrained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb datase... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467... | {"language": ["en"], "tags": ["summarization"], "datasets": ["ccdv/mediasum"], "metrics": ["rouge"], "model-index": [{"name": "ccdv/lsg-bart-base-16384-mediasum", "results": []}]} | ccdv/lsg-bart-base-16384-mediasum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"custom_code",
"en",
"dataset:ccdv/mediasum",
"arxiv:2210.15497",
"autotrain_compatible",
"region:us"
] | null | 2022-06-23T15:09:06+00:00 | [
"2210.15497"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #custom_code #en #dataset-ccdv/mediasum #arxiv-2210.15497 #autotrain_compatible #region-us
| Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust\_remote\_code=True
See #13467
LSG ArXiv paper.
Github/conversion script is available at this link.
ccdv/lsg-bart-base-16384-mediasum
=================================
This model is a fine-tuned version of ccdv/lsg-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_t... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8e-05\n* train\\_batch\\_size... |
null | null |
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone http... | {"tags": ["opt_metasq"]} | ArthurZ/opt-66000m | null | [
"opt_metasq",
"region:us"
] | null | 2022-06-23T15:20:29+00:00 | [] | [] | TAGS
#opt_metasq #region-us
|
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
## 2. Install Megatron
## 3. Install fairscale
## 4. Install metaseq
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
| [
"# This repo let's you run the following checkpoint using facebookresearch/metaseq.\n\nDo the following:",
"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\")",
"## 6. Run the following:"
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"TAGS\n#opt_metasq #region-us \n",
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"## 1. Install PyTorch",
"## 2. Install Megatron",
"## 3. Install fairscale",
"## 4. Install metaseq",
"## 5. Clone this repo (click top right on \"How to clone\"... |
image-classification | transformers |
# where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office
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... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | mayoughi/where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T15:28:06+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-apartment-office
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
####... | [
"# where_am_I_hospital-balcony-hallway-airport-coffee-house-apartment-office\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",
"#### ... | [
"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-apartment-office\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anythi... |
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. -->
# trained_french
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "trained_french", "results": []}]} | eugenetanjc/trained_french | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T16:15:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| trained\_french
===============
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.8493
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 12\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 6\... |
feature-extraction | sentence-transformers |
# all-mpnet-base-v2 clone
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The only difference between this model and the official one is that the `pipeline_tag: feature... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "feature-extraction"} | guidecare/all-mpnet-base-v2-feature-extraction | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T19:11:48+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us
| all-mpnet-base-v2 clone
=======================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The only difference between this model and the official one is that the 'pipeline\_tag: feature-... | [
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sent... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'microsoft/mpnet-base' model. ... |
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-squadV2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an un... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-end2end-questions-generation-squadV2", "results": []}]} | wiselinjayajos/t5-end2end-questions-generation-squadV2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T19:32:03+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-end2end-questions-generation-squadV2
This model is a fine-tuned version of t5-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# t5-end2end-questions-generation-squadV2\n\nThis model is a fine-tuned version of t5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-end2end-questions-generation-squadV2\n\nThis model is a fine-tuned version of t5-base on an unknown dataset.",
"## Mode... |
null | spacy | Turkish floret vectors
| Feature | Description |
| --- | --- |
| **Name** | `tr_floret_web_md` |
| **Version** | `3.5.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | |
| **Components** | |
| **Vectors** | -1 keys, 50000 unique vectors (300 dimensions) |
| **Sources** | [OSCAR Corpus 21.09](https://osc... | {"language": ["tr"], "license": "mit", "tags": ["spacy"], "model-index": [{"name": "tr_floret_web_md", "results": []}]} | spacyturk/tr_floret_web_md | null | [
"spacy",
"tr",
"license:mit",
"region:us"
] | null | 2022-06-23T20:11:25+00:00 | [] | [
"tr"
] | TAGS
#spacy #tr #license-mit #region-us
| Turkish floret vectors
| [] | [
"TAGS\n#spacy #tr #license-mit #region-us \n"
] |
null | spacy | Turkish floret vectors
| Feature | Description |
| --- | --- |
| **Name** | `tr_floret_web_lg` |
| **Version** | `3.5.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | |
| **Components** | |
| **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
| **Sources** | [OSCAR Corpus 21.09](https://os... | {"language": ["tr"], "license": "mit", "tags": ["spacy"], "model-index": [{"name": "tr_floret_web_lg", "results": []}]} | spacyturk/tr_floret_web_lg | null | [
"spacy",
"tr",
"license:mit",
"region:us"
] | null | 2022-06-23T20:25:33+00:00 | [] | [
"tr"
] | TAGS
#spacy #tr #license-mit #region-us
| Turkish floret vectors
| [] | [
"TAGS\n#spacy #tr #license-mit #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. -->
# layoutlmv3-finetuned-invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/... | {"tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "args": "sroie"}... | oussama/layoutlmv3-finetuned-invoice | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-23T20:29:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| layoutlmv3-finetuned-invoice
============================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0018
* Precision: 1.0
* Recall: 1.0
* F1: 1.0
* Accuracy: 1.0
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 2000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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... | yellajaswanth/Test-LunarLander-PPO | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-23T20:32:45+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
# Twin-Tailed Fabio DialoGPT Model
| {"tags": ["conversational"]} | Averium/FabioBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T20:45:21+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Twin-Tailed Fabio DialoGPT Model
| [
"# Twin-Tailed Fabio DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Twin-Tailed Fabio DialoGPT Model"
] |
text-generation | transformers |
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OP... | {"language": "en", "license": "other", "tags": ["text-generation", "opt"], "inference": false, "commercial": false} | facebook/opt-66b | null | [
"transformers",
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"license:other",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-06-23T20:51:55+00:00 | [
"2205.01068",
"2005.14165"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #has_space #text-generation-inference #region-us
|
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.
Disclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper.
Content... | [
"# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.\n\nDisclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. ... | [
"TAGS\n#transformers #pytorch #tf #jax #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language... |
sentence-similarity | sentence-transformers |
## `semanlink_all_mpnet_base_v2`
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
`semanlink_all_mpnet_base_v2` has been fine-tuned on the knowledge graph [Semanlink](http://www.semanlink.net/sl... | {"language": ["en", "fr"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | raphaelsty/semanlink_all_mpnet_base_v2 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"fr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T21:58:22+00:00 | [] | [
"en",
"fr"
] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #fr #license-apache-2.0 #endpoints_compatible #region-us
|
## 'semanlink_all_mpnet_base_v2'
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
'semanlink_all_mpnet_base_v2' has been fine-tuned on the knowledge graph Semanlink via the library MKB on the li... | [
"## 'semanlink_all_mpnet_base_v2'\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\n'semanlink_all_mpnet_base_v2' has been fine-tuned on the knowledge graph Semanlink via the library MKB o... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #en #fr #license-apache-2.0 #endpoints_compatible #region-us \n",
"## 'semanlink_all_mpnet_base_v2'\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be use... |
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="tuhina13/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"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": ... | tuhina13/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-23T22:29:53+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | rcanand/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-23T22:31:09+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tuhina13/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": "7.44 +/... | tuhina13/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-23T22:36:28+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# prahlad/rotten_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on rotten_tom... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "prahlad/rotten_model", "results": []}]} | prahlad/rotten_model | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T22:46:48+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| prahlad/rotten\_model
=====================
This model is a fine-tuned version of bert-base-uncased on rotten\_tomatoes movie review dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4876
* Train Accuracy: 0.7620
* Validation Loss: 0.5001
* Validation Accuracy: 0.7842
* Epoch: 0
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 12795, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'cla... |
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="rcanand/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": ... | rcanand/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-23T23:11:56+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="rcanand/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": "7.32 +/... | rcanand/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-23T23:16:06+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twisent_twisent
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/s... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "twisent_twisent", "results": []}]} | shatabdi/twisent_twisent | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T23:26:42+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# twisent_twisent
This model is a fine-tuned version of siebert/sentiment-roberta-large-english on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# twisent_twisent\n\nThis model is a fine-tuned version of siebert/sentiment-roberta-large-english on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# twisent_twisent\n\nThis model is a fine-tuned version of siebert/sentiment-roberta-large-english on an unknown dataset.",
"## Model description\n\nMore information 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. -->
# twisent_sieBert
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/s... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "twisent_sieBert", "results": []}]} | shatabdi/twisent_sieBert | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-23T23:51:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# twisent_sieBert
This model is a fine-tuned version of siebert/sentiment-roberta-large-english on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# twisent_sieBert\n\nThis model is a fine-tuned version of siebert/sentiment-roberta-large-english on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# twisent_sieBert\n\nThis model is a fine-tuned version of siebert/sentiment-roberta-large-english on an unknown dataset.",
"## Model description\n\nMore information n... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | rcanand/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-24T00:37:14+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | transformers | ## Dataset Summary
- **Homepage:** https://salt-nlp.github.io/FLANG/
- **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT
- **Repository:** https://github.com/SALT-NLP/FLANG
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferentia... | {"language": "en", "tags": ["Financial Language Modelling", "financial-sentiment-analysis"], "widget": [{"text": "Stocks rallied and the British pound <mask>."}]} | SALT-NLP/FLANG-ELECTRA | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"Financial Language Modelling",
"financial-sentiment-analysis",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T00:45:20+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #electra #pretraining #Financial Language Modelling #financial-sentiment-analysis #en #endpoints_compatible #region-us
| ## Dataset Summary
- Homepage: URL
- Models: URL
- Repository: URL
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\
FLANG-BERT\
FLANG-Sp... | [
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\\\nFLANG... | [
"TAGS\n#transformers #pytorch #electra #pretraining #Financial Language Modelling #financial-sentiment-analysis #en #endpoints_compatible #region-us \n",
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. Thes... |
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. -->
# kw_pubmed_vanilla_sentence_10000_0.0003_2
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abs... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["enoriega/keyword_pubmed"], "metrics": ["accuracy"], "model-index": [{"name": "kw_pubmed_vanilla_sentence_10000_0.0003_2", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "enoriega/keyword_pubmed sent... | enoriega/kw_pubmed_vanilla_sentence_10000_0.0003_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:enoriega/keyword_pubmed",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T00:52:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-enoriega/keyword_pubmed #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# kw_pubmed_vanilla_sentence_10000_0.0003_2
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the enoriega/keyword_pubmed sentence dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5883
- Accuracy: 0.6767
## Model description
More info... | [
"# kw_pubmed_vanilla_sentence_10000_0.0003_2\n\nThis model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the enoriega/keyword_pubmed sentence dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.5883\n- Accuracy: 0.6767",
"## Model description... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-enoriega/keyword_pubmed #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# kw_pubmed_vanilla_sentence_10000_0.0003_2\n\nThis model is a fine-tuned version of microsoft/BiomedNLP-PubMe... |
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. -->
# wav2vec_mle
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-96... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec_mle", "results": []}]} | eugenetanjc/wav2vec_mle | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T01:12:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_mle
============
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.3076
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 12\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 6... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | ManqingLiu/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T01:27:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1709
* Accuracy: 0.9305
* F1: 0.9306
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-generation | transformers |
# Try my rick, it responds. | {"tags": ["conversational"]} | arem/DialoGPT-medium-rickandmorty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T01:30:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Try my rick, it responds. | [
"# Try my rick, it responds."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Try my rick, it responds."
] |
fill-mask | transformers | ## Dataset Summary
- **Homepage:** https://salt-nlp.github.io/FLANG/
- **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT
- **Repository:** https://github.com/SALT-NLP/FLANG
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferentia... | {"language": "en", "tags": ["Financial Language Modelling"], "widget": [{"text": "Stocks rallied and the British pound [MASK]."}]} | SALT-NLP/FLANG-BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Financial Language Modelling",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T01:37:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #Financial Language Modelling #en #autotrain_compatible #endpoints_compatible #region-us
| ## Dataset Summary
- Homepage: URL
- Models: URL
- Repository: URL
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\
FLANG-BERT\
FLANG-Sp... | [
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\\\nFLANG... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Financial Language Modelling #en #autotrain_compatible #endpoints_compatible #region-us \n",
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These models use ... |
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. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]} | ferzimo/dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T02:36:09+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
null | PyTorch Lightning |
## Model Details
This model is from [FSPBT-Image-Translation](https://github.com/rnwzd/FSPBT-Image-Translation)
## Citation Information
```bibtex
@Article{Texler20-SIG,
author = "Ond\v{r}ej Texler and David Futschik and Michal Ku\v{c}era and Ond\v{r}ej Jamri\v{s}ka and \v{S}\'{a}rka Sochorov\'{a} and Mengl... | {"license": "mit", "library_name": "PyTorch Lightning", "tags": ["Image Translation"]} | BigDL/FSPBT | null | [
"PyTorch Lightning",
"Image Translation",
"license:mit",
"has_space",
"region:us"
] | null | 2022-06-24T03:05:06+00:00 | [] | [] | TAGS
#PyTorch Lightning #Image Translation #license-mit #has_space #region-us
|
## Model Details
This model is from FSPBT-Image-Translation
| [
"## Model Details\nThis model is from FSPBT-Image-Translation"
] | [
"TAGS\n#PyTorch Lightning #Image Translation #license-mit #has_space #region-us \n",
"## Model Details\nThis model is from FSPBT-Image-Translation"
] |
null | null | import gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.utils import set_random_seed
def make_env(env_id, rank, seed=0):
"""
Utility function... | {} | Saraswati/Stable_Baselines3 | null | [
"region:us"
] | null | 2022-06-24T04:02:47+00:00 | [] | [] | TAGS
#region-us
| import gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.URL import set_random_seed
def make_env(env_id, rank, seed=0):
"""
Utility function for mult... | [
"# Number of processes to use\n # Create the vectorized environment\n env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])\n\n # Stable Baselines provides you with make_vec_env() helper\n # which does exactly the previous steps for you.\n # You can choose between 'DummyVecEnv' (usually ... | [
"TAGS\n#region-us \n",
"# Number of processes to use\n # Create the vectorized environment\n env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])\n\n # Stable Baselines provides you with make_vec_env() helper\n # which does exactly the previous steps for you.\n # You can choose betwee... |
translation | transformers | ### en-he
* source group: English
* target group: Hebrew
* OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md)
* model: transformer-align
* source language(s): eng
* target language(s): heb
* model: transformer-align
* pre-processing: normalization + Sent... | {"language": ["en", "he"], "license": "apache-2.0", "tags": ["translation"]} | Rahulrr/language_model_en_he | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"he",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T04:28:35+00:00 | [] | [
"en",
"he"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### en-he
* source group: English
* target group: Hebrew
* OPUS readme: eng-heb
* model: transformer-align
* source language(s): eng
* target language(s): heb
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: opus+URL
* test set translations: opus+... | [
"### en-he\n\n\n* source group: English\n* target group: Hebrew\n* OPUS readme: eng-heb\n* model: transformer-align\n* source language(s): eng\n* target language(s): heb\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: opus+URL\n* test set tr... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #he #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### en-he\n\n\n* source group: English\n* target group: Hebrew\n* OPUS readme: eng-heb\n* model: transformer-align\n* source language(s): eng\n* target ... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | jesse-lopez/classify-fish-sounds | null | [
"fastai",
"region:us"
] | null | 2022-06-24T04:31:28+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
null | fastai |
This model was trained to as part of collaboration between [Mote Marine Laboratory & Aquarium](https://mote.org), [Southeast Coastal Ocean Observing Regional Association](https://secoora.org), and [Axiom Data Science](https://axiomdatascience.com) to develop a model capable of detecting and classifying fish vocalizati... | {"tags": ["fastai"]} | axds/classify-fish-sounds | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-06-24T04:33:58+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
| This model was trained to as part of collaboration between Mote Marine Laboratory & Aquarium, Southeast Coastal Ocean Observing Regional Association, and Axiom Data Science to develop a model capable of detecting and classifying fish vocalizations from audio files collected from hydrophones.
More information availabl... | [
"### Class label description",
"### Class indices in trained model\n\n\nSome classes did not meet the training criteria, high signal-to-noise ratio and minimum call overlap, and were therefore excluded from the model training.\nAs such, the number of classes represented in the trained model is few than the amount... | [
"TAGS\n#fastai #has_space #region-us \n",
"### Class label description",
"### Class indices in trained model\n\n\nSome classes did not meet the training criteria, high signal-to-noise ratio and minimum call overlap, and were therefore excluded from the model training.\nAs such, the number of classes represented... |
fill-mask | transformers | ## Dataset Summary
- **Homepage:** https://salt-nlp.github.io/FLANG/
- **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT
- **Repository:** https://github.com/SALT-NLP/FLANG
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferentia... | {"language": "en", "tags": ["Financial Language Modelling"], "widget": [{"text": "Stocks rallied and the British pound [MASK]."}]} | SALT-NLP/FLANG-SpanBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"Financial Language Modelling",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T04:41:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #Financial Language Modelling #en #autotrain_compatible #endpoints_compatible #region-us
| ## Dataset Summary
- Homepage: URL
- Models: URL
- Repository: URL
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\
FLANG-BERT\
FLANG-Sp... | [
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\\\nFLANG... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #Financial Language Modelling #en #autotrain_compatible #endpoints_compatible #region-us \n",
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These models use ... |
fill-mask | transformers | ## Dataset Summary
- **Homepage:** https://salt-nlp.github.io/FLANG/
- **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT
- **Repository:** https://github.com/SALT-NLP/FLANG
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferentia... | {"language": "en", "tags": ["Financial Language Modelling"], "widget": [{"text": "Stocks rallied and the British pound [MASK]."}]} | SALT-NLP/FLANG-DistilBERT | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"Financial Language Modelling",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T04:43:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #Financial Language Modelling #en #autotrain_compatible #endpoints_compatible #region-us
| ## Dataset Summary
- Homepage: URL
- Models: URL
- Repository: URL
## FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\
FLANG-BERT\
FLANG-Sp... | [
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\\\nFLANG... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #Financial Language Modelling #en #autotrain_compatible #endpoints_compatible #region-us \n",
"## Dataset Summary\n- Homepage: URL\n- Models: URL\n- Repository: URL",
"## FLANG\nFLANG is a set of large language models for Financial LANGuage tasks. These model... |
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-v2
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer"], "model-index": [{"name": "ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2", "results": []}]} | gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T05:09:39+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-v2
========================================================
This model is a fine-tuned version of gary109/ai-light-dance\_stepmania\_ft\_wav2vec2-large-xlsr-53-v1 on the GARY109/AI\_LIGHT\_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 1e-05\n* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jwang/tuned-t5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves th... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jwang/tuned-t5", "results": []}]} | jwang/tuned-t5 | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T05:16:12+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| jwang/tuned-t5
==============
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.6386
* Validation Loss: 3.3773
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
text-generation | transformers |
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_... | {"language": "en", "license": "mit", "tags": ["exbert"]} | AlfredLeeee/testmodel_classifier | null | [
"transformers",
"jax",
"tflite",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T05:39:58+00:00 | [] | [
"en"
] | TAGS
#transformers #jax #tflite #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #jax #tflite #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset... |
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": []}]} | Guo-Zikun/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T06:04:19+00:00 | [] | [] | TAGS
#transformers #pytorch #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 #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.",
"## Model description... |
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... | Lakshya/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-24T06:45:16+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
# Chatty Rick DialoGBT Model | {"tags": ["conversational"]} | soProf1998/DialoGPT-small-chattyrick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T06:48:07+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Chatty Rick DialoGBT Model | [
"# Chatty Rick DialoGBT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chatty Rick DialoGBT Model"
] |
text-classification | transformers | first test model om Huggingface HUB | {} | Mraleksa/fine-tune-distilbert-exitru | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T06:49:54+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| first test model om Huggingface HUB | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | https://www.humhealth.com/remote-patient-monitoring/
https://www.humhealth.com/chronic-care-management/
| {"license": "bsl-1.0"} | humhealth/remote-patientmonitoring | null | [
"license:bsl-1.0",
"region:us"
] | null | 2022-06-24T07:07:20+00:00 | [] | [] | TAGS
#license-bsl-1.0 #region-us
| URL
URL
| [] | [
"TAGS\n#license-bsl-1.0 #region-us \n"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | AlexChe/MLAgents-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-06-24T07:12:08+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
null | null | https://www.humhealth.com/chronic-care-management/ | {"license": "bsl-1.0"} | humhealth/chroniccaremanagement | null | [
"license:bsl-1.0",
"region:us"
] | null | 2022-06-24T07:14:24+00:00 | [] | [] | TAGS
#license-bsl-1.0 #region-us
| URL | [] | [
"TAGS\n#license-bsl-1.0 #region-us \n"
] |
text-generation | transformers |
# DialoGPT Trained on the Speech of Rick from [The Show Rick & Morty]
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a character speech.
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoToke... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png"} | soProf1998/DialoGPT-medium-chattyrick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T07:40:30+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of Rick from [The Show Rick & Morty]
This is an instance of microsoft/DialoGPT-medium trained on a character speech.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of Rick from [The Show Rick & Morty]\n\nThis is an instance of microsoft/DialoGPT-medium trained on a character speech.\n\nChat with the model:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of Rick from [The Show Rick & Morty]\n\nThis is an instance of microsoft/DialoGPT-medium trained on a character... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | IsaMaks/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T07:41:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8874
* Precision: 0.2534
* Recall: 0.3333
* F1: 0.2879
* Accuracy: 0.7603
* True predictio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikisql
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-wikisql", "results": []}]} | mousaazari/t5-small-finetuned-wikisql | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T08:47:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikisql
==========================
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2640
* Rouge2 Precision: 0.8471
* Rouge2 Recall: 0.3841
* Rouge2 Fmeasure: 0.5064
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-radarr
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["movie_releases"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-radarr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "movie_releases... | Servarr/bert-finetuned-radarr | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:movie_releases",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-24T08:52:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-movie_releases #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-radarr
=====================
This model is a fine-tuned version of distilbert-base-uncased on the movie\_releases dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0731
* Precision: 0.9555
* Recall: 0.9639
* F1: 0.9597
* Accuracy: 0.9818
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-movie_releases #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652
## Validation Metrics
- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278
## Usage
You c... | {"language": "en", "tags": ["autotrain"], "datasets": ["lewtun/autotrain-data-acronym-identification", "acronym_identification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 10.435358044493652, "model-index": [{"name": "autotrain-demo", "results": [{"task": {"type": "token-classification"... | lewtun/autotrain-acronym-identification-7324788 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:lewtun/autotrain-data-acronym-identification",
"dataset:acronym_identification",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-24T09:11:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-lewtun/autotrain-data-acronym-identification #dataset-acronym_identification #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 7324788
- CO2 Emissions (in grams): 10.435358044493652
## Validation Metrics
- Loss: 0.08991389721632004
- Accuracy: 0.9708090976211485
- Precision: 0.8998421675654347
- Recall: 0.9309429854401959
- F1: 0.9151284109149278
## Usage
You c... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 7324788\n- CO2 Emissions (in grams): 10.435358044493652",
"## Validation Metrics\n\n- Loss: 0.08991389721632004\n- Accuracy: 0.9708090976211485\n- Precision: 0.8998421675654347\n- Recall: 0.9309429854401959\n- F1: 0.915128410914927... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-lewtun/autotrain-data-acronym-identification #dataset-acronym_identification #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: E... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# amorfati/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-s... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "amorfati/mt5-small-finetuned-amazon-en-es", "results": []}]} | amorfati/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-24T09:12:41+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| amorfati/mt5-small-finetuned-amazon-en-es
=========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.7070
* Validation Loss: 2.5179
* 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': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 200000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycl... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
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... | ataunal/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-24T09:41:36+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... |
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