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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="arpitvaghela/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False et... | {"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 +/... | arpitvaghela/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-19T13:35:21+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 85.8 | 85.9 |
| test | 84.2 | 84.3 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-xnli_fr_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T13:35:57+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 85.8, F1macro: 85.9
Set: test, F1micro: 84.2, F1macro: 84.3
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | transformers |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'Lunar... | {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2... | kambehmw/PPO-LunarLander-v2 | null | [
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T13:59:48+00:00 | [] | [] | TAGS
#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters"
] | [
"TAGS\n#transformers #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #endpoints_compatible #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # H... |
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. -->
# mt5-small-amrit-finetuned-amazon-en
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-s... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-amrit-finetuned-amazon-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {... | amritpattnaik/mt5-small-amrit-finetuned-amazon-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T14:38:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-amrit-finetuned-amazon-en
===================================
This model is a fine-tuned version of google/mt5-small on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3112
* Rouge1: 15.4603
* Rouge2: 7.1882
* Rougel: 15.2221
* Rougelsum: 15.1231
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperp... |
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. -->
# lead-reliability-scoring
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-m... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "lead-reliability-scoring", "results": []}]} | thaidv96/lead-reliability-scoring | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T14:44:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| lead-reliability-scoring
========================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0123
* F1: 0.9937
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
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-cased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-cased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll... | swardiantara/distilbert-base-cased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T14:47:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-cased-finetuned-ner
===================================
This model is a fine-tuned version of distilbert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0709
* Precision: 0.9170
* Recall: 0.9272
* F1: 0.9221
* Accuracy: 0.9804
Model descripti... | [
"### 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 #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
text-generation | transformers |
# DialoGPT Model based on my sms history | {"tags": ["conversational"]} | Sealgair/DialoGPT-medium-Eyden | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T15:03:37+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Model based on my sms history | [
"# DialoGPT Model based on my sms history"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Model based on my sms history"
] |
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"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | mo7amed3ly/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T15:30:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0590
* Precision: 0.9270
* Recall: 0.9399
* F1: 0.9334
* Accuracy: 0.9844
Model des... | [
"### 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 #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
null | null | Emojiandlemon | {"license": "apache-2.0"} | Amaralisma/Emoji | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-06-19T15:34:45+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Emojiandlemon | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
text-classification | transformers |
# Danish-Bert-GoÆmotion
Danish Go-Emotions classifier. [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (uncased) finetuned on a translation of the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki-NLP/opu... | {"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "sentiment", "text-classification", "Maltehb/danish-bert-botxo", "Helsinki-NLP/opus-mt-en-da", "go-emotion", "Certainly"], "datasets": ["go_emotions"], "metrics": ["Accuracy"], "widget": [{"text": "Det er s\u00e5 s\u00f8dt af dig at t\u00e6nke p\u00e... | RJuro/Da-HyggeBERT | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"danish",
"sentiment",
"Maltehb/danish-bert-botxo",
"Helsinki-NLP/opus-mt-en-da",
"go-emotion",
"Certainly",
"da",
"dataset:go_emotions",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T16:41:42+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #bert #text-classification #danish #sentiment #Maltehb/danish-bert-botxo #Helsinki-NLP/opus-mt-en-da #go-emotion #Certainly #da #dataset-go_emotions #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish-Bert-GoÆmotion
Danish Go-Emotions classifier. Maltehb/danish-bert-botxo (uncased) finetuned on a translation of the go_emotions dataset using Helsinki-NLP/opus-mt-en-da. Thus, performance is obviousely dependent on the translation model.
## Training
- Translating the training data with MT: Notebook
- Fine-t... | [
"# Danish-Bert-GoÆmotion\n\nDanish Go-Emotions classifier. Maltehb/danish-bert-botxo (uncased) finetuned on a translation of the go_emotions dataset using Helsinki-NLP/opus-mt-en-da. Thus, performance is obviousely dependent on the translation model.",
"## Training\n- Translating the training data with MT: Notebo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #danish #sentiment #Maltehb/danish-bert-botxo #Helsinki-NLP/opus-mt-en-da #go-emotion #Certainly #da #dataset-go_emotions #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish-Bert-GoÆmotion\n\nDanish Go-Emotions classifie... |
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/869087268560134144/cn6Lu... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/aktualnecz-lidovky-respekt_cz | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T16:46:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
URL & Aktuálně.cz & Týdeník Respekt
@aktualnecz-lidovky-respekt\_cz
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, chec... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Nikkisora/PPO_LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-19T16:52:38+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1523817638706700288/tVCx... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/notch/1655661312216/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/notch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T16:54:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Notch
@notch
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# RE-SepFormer trained on WSJ0-2Mix
This repository provides all the necessary tools to perform audio source... | {"language": "en", "license": "apache-2.0", "tags": ["Source Separation", "Speech Separation", "Audio Source Separation", "WSJ02Mix", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation", "speechbrain"], "datasets": ["WSJ0-2Mix"], "metrics": ["SI-SNRi", "SDRi"]} | speechbrain/resepformer-wsj02mix | null | [
"speechbrain",
"Source Separation",
"Speech Separation",
"Audio Source Separation",
"WSJ02Mix",
"SepFormer",
"Transformer",
"audio-to-audio",
"audio-source-separation",
"en",
"dataset:WSJ0-2Mix",
"arxiv:2206.09507",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-06-19T17:08:32+00:00 | [
"2206.09507",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2206.09507 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
RE-SepFormer trained on WSJ0-2Mix
=================================
This repository provides all the necessary tools to perform audio source separation with a RE-SepFormer
model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about
Spe... | [
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings sampled at 8kHz (single channel).\nIf your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'ru... | [
"TAGS\n#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2206.09507 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Perform source separation on your own audio file\n\n\nTh... |
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/1531198610129428480/Gopl... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/g2esports/1655664936018/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/g2esports | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T17:08:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
G2 Esports
@g2esports
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prophetnet-large-squad
This model is a fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co/microso... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "prophetnet-large-squad", "results": []}]} | anas-awadalla/prophetnet-large-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"prophetnet",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T17:12:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #prophetnet #text2text-generation #generated_from_trainer #dataset-squad #autotrain_compatible #endpoints_compatible #region-us
|
# prophetnet-large-squad
This model is a fine-tuned version of microsoft/prophetnet-large-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
### Traini... | [
"# prophetnet-large-squad\n\nThis model is a fine-tuned version of microsoft/prophetnet-large-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",
"## Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #prophetnet #text2text-generation #generated_from_trainer #dataset-squad #autotrain_compatible #endpoints_compatible #region-us \n",
"# prophetnet-large-squad\n\nThis model is a fine-tuned version of microsoft/prophetnet-large-uncased on the squad dataset.",
"## Model ... |
text-classification | transformers |
### Welcome to ParlBERT-Topic-German!
🏷 **Model description**
This model was trained on \~10k manually annotated interpellations (📚 [Breunig/ Schnatterer 2019](https://oxford.universitypressscholarship.com/view/10.1093/oso/9780198835332.001.0001/oso-9780198835332)) with topics from the [Comparative Agendas Project... | {"language": "de", "widget": [{"text": "Das Sachgebiet Investive Ausgaben des Bundes Bundesfinanzminister Apel hat gem\u00e4\u00df BMF Finanznachrichten vom 1. Januar erkl\u00e4rt, die Investitionsquote des Bundes sei in den letzten zehn Jahren nahezu konstant geblieben."}]} | chkla/parlbert-topic-german | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-19T17:20:29+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Welcome to ParlBERT-Topic-German!
Model description
This model was trained on ~10k manually annotated interpellations ( Breunig/ Schnatterer 2019) with topics from the Comparative Agendas Project to classify text into one of twenty labels (annotation codebook).
*Note: "Interpellation is a formal request of a ... | [
"### Welcome to ParlBERT-Topic-German!\n\n\nModel description\n\n\nThis model was trained on ~10k manually annotated interpellations ( Breunig/ Schnatterer 2019) with topics from the Comparative Agendas Project to classify text into one of twenty labels (annotation codebook).\n\n\n*Note: \"Interpellation is a forma... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Welcome to ParlBERT-Topic-German!\n\n\nModel description\n\n\nThis model was trained on ~10k manually annotated interpellations ( Breunig/ Schnatterer 2019) with to... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1004733283
- CO2 Emissions (in grams): 10.8014599472142
## Validation Metrics
- Loss: 0.00010539647337282076
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weigh... | {"language": "en", "tags": "autotrain", "datasets": ["twhitehurst3/autotrain-data-blaze_text_classification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 10.8014599472142} | twhitehurst3/autotrain-blaze_text_classification-1004733283 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:twhitehurst3/autotrain-data-blaze_text_classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T17:59:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-twhitehurst3/autotrain-data-blaze_text_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1004733283
- CO2 Emissions (in grams): 10.8014599472142
## Validation Metrics
- Loss: 0.00010539647337282076
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weigh... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1004733283\n- CO2 Emissions (in grams): 10.8014599472142",
"## Validation Metrics\n\n- Loss: 0.00010539647337282076\n- Accuracy: 1.0\n- Macro F1: 1.0\n- Micro F1: 1.0\n- Weighted F1: 1.0\n- Macro Precision: 1.0\n- Micro Pr... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-twhitehurst3/autotrain-data-blaze_text_classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 100473328... |
fill-mask | transformers | Finetuned model of the BerTweet-base model. Bad performance, should not be used as such. The tokenizer of BerTweet-base has to be used. | {} | nilaB97/bertweet-refugee | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T18:05:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Finetuned model of the BerTweet-base model. Bad performance, should not be used as such. The tokenizer of BerTweet-base has to be used. | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | martin-ha/vision_encoder_in_dual | null | [
"keras",
"region:us"
] | null | 2022-06-19T18:06:52+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | martin-ha/text_encoder_in_dual | null | [
"keras",
"region:us"
] | null | 2022-06-19T18:10:58+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
text-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/1438687880101212170/nNi2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/thenoelmiller/1655666288084/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/thenoelmiller | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T18:16:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Noel Miller
@thenoelmiller
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | 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... | voleg44/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-19T19:05:54+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... |
fill-mask | transformers |
### CosmicRoBERTa
This model is a further pre-trained version of RoBERTa for space science on a domain-specific corpus, which includes abstracts from the NTRS library, abstracts from SCOPUS, ECSS requirements, and other sources from this domain.
This totals to a pre-training corpus of around 75 mio words.
The mode... | {"license": "mit", "widget": [{"text": "The closest planet to earth is <mask>."}, {"text": "Electrical power is stored on a spacecraft with <mask>."}]} | icelab/cosmicroberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T20:26:52+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### CosmicRoBERTa
This model is a further pre-trained version of RoBERTa for space science on a domain-specific corpus, which includes abstracts from the NTRS library, abstracts from SCOPUS, ECSS requirements, and other sources from this domain.
This totals to a pre-training corpus of around 75 mio words.
The model... | [
"### CosmicRoBERTa\n\n\nThis model is a further pre-trained version of RoBERTa for space science on a domain-specific corpus, which includes abstracts from the NTRS library, abstracts from SCOPUS, ECSS requirements, and other sources from this domain.\nThis totals to a pre-training corpus of around 75 mio words.\n\... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### CosmicRoBERTa\n\n\nThis model is a further pre-trained version of RoBERTa for space science on a domain-specific corpus, which includes abstracts from the NTRS library, abstracts from SC... |
image-classification | transformers |
# generation_xyz
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | chradden/generation_xyz | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T20:33:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# generation_xyz
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
#### Baby Boomers
!Baby Boomers
#### Generation Alpha
!Generation Alpha
#### Generation X
!Generation X
#... | [
"# generation_xyz\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",
"#### Baby Boomers\n\n!Baby Boomers",
"#### Generation Alpha\n\n!Generation Alpha",
"#### G... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# generation_xyz\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issue... |
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... | sevlabr/unit-1-PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-19T20:51:58+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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/1542935688026370048/DofQ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/soundersfc/1656893134824/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/soundersfc | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T21:04:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Seattle Sounders FC
@soundersfc
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509050806795964416/g7Fe... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/carboxylace/1655678588553/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/carboxylace | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T21:41:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
lace
@carboxylace
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/borisjohnson-elonmusk-majornelson/1655678567047/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/borisjohnson-elonmusk-majornelson | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T21:42:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Larry Hryb 🇺🇦 & Boris Johnson
@borisjohnson-elonmusk-majornelson
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 deve... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1486761402853380113/3ifA... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/fabrizioromano/1655681846804/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/fabrizioromano | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T22:24:59+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Fabrizio Romano
@fabrizioromano
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1522592324785557504/ylln... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bts_twt/1663110994160/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bts_twt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T22:52:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
방탄소년단
@bts\_twt
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Nishiki Chatbot Model | {"tags": ["conversational"]} | crystallyzing/DialoGPT-small-nishikiyama | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-19T22:53:16+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Nishiki Chatbot Model | [
"# Nishiki Chatbot Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Nishiki Chatbot Model"
] |
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. -->
# prompt-tuned-t5-base-num-tokens-100-squad
This model is a fine-tuned version of [google/t5-base-lm-adapt](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "prompt-tuned-t5-base-num-tokens-100-squad", "results": []}]} | anas-awadalla/prompt-tuned-t5-base-num-tokens-100-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T23:44:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# prompt-tuned-t5-base-num-tokens-100-squad
This model is a fine-tuned version of google/t5-base-lm-adapt on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# prompt-tuned-t5-base-num-tokens-100-squad\n\nThis model is a fine-tuned version of google/t5-base-lm-adapt on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# prompt-tuned-t5-base-num-tokens-100-squad\n\nThis model is a fine-tuned version of google/t5-base-lm-adapt on the squad dataset.",
"## Model description\n\nMore informat... |
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. -->
# prompt-tuned-t5-small-num-tokens-100-squad
This model is a fine-tuned version of [google/t5-small-lm-adapt](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "prompt-tuned-t5-small-num-tokens-100-squad", "results": []}]} | anas-awadalla/prompt-tuned-t5-small-num-tokens-100-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-19T23:50:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# prompt-tuned-t5-small-num-tokens-100-squad
This model is a fine-tuned version of google/t5-small-lm-adapt on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# prompt-tuned-t5-small-num-tokens-100-squad\n\nThis model is a fine-tuned version of google/t5-small-lm-adapt on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# prompt-tuned-t5-small-num-tokens-100-squad\n\nThis model is a fine-tuned version of google/t5-small-lm-adapt on the squad dataset.",
"## Model description\n\nMore inform... |
text-generation | transformers |
# Kiryu Chatbot Model | {"tags": ["conversational"]} | crystallyzing/DialoGPT-small-kiryu | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T00:50:36+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Kiryu Chatbot Model | [
"# Kiryu Chatbot Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Kiryu Chatbot Model"
] |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-squadshifts-vanilla-reddit-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.com/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-large-squadshifts-vanilla-reddit-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T01:02:26+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-squadshifts-vanilla-reddit-qg'
======================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Language model: t5-... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTrain... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Tr... |
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/1529201641290752000/al3u... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/grassmannian | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T01:11:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Brendan era
@grassmannian
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689
# CryptoBERT
CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the [vinai's bertweet-base](https://hugg... | {"language": ["en"], "tags": ["cryptocurrency", "crypto", "BERT", "sentiment classification", "NLP", "bitcoin", "ethereum", "shib", "social media", "sentiment analysis", "cryptocurrency sentiment analysis"], "datasets": ["ElKulako/stocktwits-crypto"]} | ElKulako/cryptobert | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"cryptocurrency",
"crypto",
"BERT",
"sentiment classification",
"NLP",
"bitcoin",
"ethereum",
"shib",
"social media",
"sentiment analysis",
"cryptocurrency sentiment analysis",
"en",
"dataset:ElKulako/stocktwits-crypto",
... | null | 2022-06-20T01:29:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #cryptocurrency #crypto #BERT #sentiment classification #NLP #bitcoin #ethereum #shib #social media #sentiment analysis #cryptocurrency sentiment analysis #en #dataset-ElKulako/stocktwits-crypto #autotrain_compatible #endpoints_compatible #has_space #region-us
|
For academic reference, cite the following paper: URL
# CryptoBERT
CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a cor... | [
"# CryptoBERT\nCryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related soc... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #cryptocurrency #crypto #BERT #sentiment classification #NLP #bitcoin #ethereum #shib #social media #sentiment analysis #cryptocurrency sentiment analysis #en #dataset-ElKulako/stocktwits-crypto #autotrain_compatible #endpoints_compatible #has_space #regio... |
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/1000136690/IslandBartosz... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bartoszmilewski/1655692518288/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/bartoszmilewski | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T01:33:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bartosz Milewski
@bartoszmilewski
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
audio-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. -->
# wav2vec2-base-finetuned-digits
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-digits", "results": []}]} | skpawar1305/wav2vec2-base-finetuned-digits | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T01:39:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-digits
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0605
* Accuracy: 0.9846
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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #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\... |
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | raesti/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T01:57:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2886
* Bleu: 28.1507
* Gen Len: 34.1136
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-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/1510046460556980225/LEbm... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/alpha_convert/1655696345558/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/alpha_convert | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T02:37:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Joe Cutler
@alpha\_convert
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | k2 |
# Introduction
See <https://github.com/k2-fsa/icefall/pull/436>
| {"language": ["zh"], "library_name": "k2", "tags": ["automatic-speech-recognition"]} | csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20 | null | [
"k2",
"tensorboard",
"automatic-speech-recognition",
"zh",
"region:us"
] | null | 2022-06-20T02:43:48+00:00 | [] | [
"zh"
] | TAGS
#k2 #tensorboard #automatic-speech-recognition #zh #region-us
|
# Introduction
See <URL
| [
"# Introduction\n\nSee <URL"
] | [
"TAGS\n#k2 #tensorboard #automatic-speech-recognition #zh #region-us \n",
"# Introduction\n\nSee <URL"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | qgrantq/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T04:30:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
text-generation | transformers |
# Billy DialoGPT Model | {"tags": ["conversational"]} | NikkiTiredAf/DialoGPT-small-billy2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T04:41:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Billy DialoGPT Model | [
"# Billy DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Billy DialoGPT Model"
] |
text-classification | fastai |
# Malayalam (മലയാളം) Classifier using fastai (Working in Progress)
🥳 This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from [Malayalam Text Classifier](https://kurianbenoy.com/2022-05-30-malayalamtext-0/). Courtesy to @waydegilliam for [blurr](https://ohmeow.github.io/blurr... | {"tags": ["fastai", "text-classification"], "datasets": "rajeshradhakrishnan/malayalam_news", "widget": [{"text": "\u0d13\u0d39\u0d30\u0d3f \u0d35\u0d3f\u0d2a\u0d23\u0d3f \u0d24\u0d15\u0d30\u0d41\u0d2e\u0d4d\u0d2a\u0d4b\u0d33\u0d4d\u200d \u0d28\u0d3f\u0d15\u0d4d\u0d37\u0d47\u0d2a\u0d02 \u0d0e\u0d19\u0d4d\u0d19\u0d28\u0... | hugginglearners/ml-news-classify-fastai | null | [
"fastai",
"text-classification",
"dataset:rajeshradhakrishnan/malayalam_news",
"has_space",
"region:us"
] | null | 2022-06-20T04:46:49+00:00 | [] | [] | TAGS
#fastai #text-classification #dataset-rajeshradhakrishnan/malayalam_news #has_space #region-us
|
# Malayalam (മലയാളം) Classifier using fastai (Working in Progress)
This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from Malayalam Text Classifier. Courtesy to @waydegilliam for blurr
മലയാളത്തിൽ മെഷീൻ ലീർണിങ് പഠിക്കാനും പിന്നേ പരിചയപ്പെടാനും, to be continued...
# How it... | [
"# Malayalam (മലയാളം) Classifier using fastai (Working in Progress)\n\n This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from Malayalam Text Classifier. Courtesy to @waydegilliam for blurr\n\n മലയാളത്തിൽ മെഷീൻ ലീർണിങ് പഠിക്കാനും പിന്നേ പരിചയപ്പെടാനും, to be continued...",
... | [
"TAGS\n#fastai #text-classification #dataset-rajeshradhakrishnan/malayalam_news #has_space #region-us \n",
"# Malayalam (മലയാളം) Classifier using fastai (Working in Progress)\n\n This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from Malayalam Text Classifier. Courtesy to... |
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/2215576731/ars-logo_400x... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/arstechnica/1655705137296/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/arstechnica | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T05:03:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ars Technica
@arstechnica
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-cased-finetuned-NLP-IE-4
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](htt... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-NLP-IE-4", "results": []}]} | Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T06:09:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-spanish-wwm-cased-finetuned-NLP-IE-4
==============================================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7825
* Accuracy: 0.4931
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 77.3 | 77.3 |
| test | 78.0 | 77.9 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-repnum_wl-rua_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T06:33:38+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 77.3, F1macro: 77.3
Set: test, F1micro: 78.0, F1macro: 77.9
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# DGMR
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation ... | {"license": "mit", "tags": ["nowcasting", "forecasting", "timeseries", "remote-sensing", "gan"]} | jacobbieker/dgmr | null | [
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T06:44:17+00:00 | [] | [] | TAGS
#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #gan #license-mit #endpoints_compatible #region-us
|
# DGMR
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation ... | [
"# DGMR",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]",
"## Limitations and bias\n\n[More information needed]",
"## Training data\n\n[More information needed]",
"## Training procedure\n\n[... | [
"TAGS\n#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #gan #license-mit #endpoints_compatible #region-us \n",
"# DGMR",
"## Model description\n\n[More information needed]",
"## Intended uses & limitations\n\n[More information needed]",
"## How to use\n\n[More information needed]... |
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. -->
# M7_MLM_final
This model is a fine-tuned version of [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "M7_MLM_final", "results": []}]} | S2312dal/M7_MLM_final | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T07:25:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| M7\_MLM\_final
==============
This model is a fine-tuned version of sentence-transformers/all-distilroberta-v1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.4732
Model description
-----------------
More information needed
Intended uses & limitations
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Malaya-speech_fine-tune_MrBrown_20_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https... | {"tags": ["generated_from_trainer"], "datasets": ["uob_singlish"], "model-index": [{"name": "Malaya-speech_fine-tune_MrBrown_20_Jun", "results": []}]} | RuiqianLi/Malaya-speech_fine-tune_MrBrown_20_Jun | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T07:59:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us
| Malaya-speech\_fine-tune\_MrBrown\_20\_Jun
==========================================
This model is a fine-tuned version of malay-huggingface/wav2vec2-xls-r-300m-mixed on the uob\_singlish dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8868
* Wer: 0.3244
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-uob_singlish #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size:... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-scratch-powo_all_pt
This model is a fine-tuned version of [](https://huggingface.co/) on the None datase... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-scratch-powo_all_pt", "results": []}]} | ViktorDo/distilbert-base-uncased-scratch-powo_all_pt | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T07:59:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-scratch-powo\_all\_pt
=============================================
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7109
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\n* eval\\_batch... |
feature-extraction | transformers | # mhubert-base
* the checkpoint converted from [textless s2st real data](https://github.com/facebookresearch/fairseq/blob/b5a039c292facba9c73f59ff34621ec131d82341/examples/speech_to_speech/docs/textless_s2st_real_data.md)
## usage:
```
asrp==0.0.35 # extracted from fairseq repo
```
```python=
# https://huggingfac... | {} | voidful/mhubert-base | null | [
"transformers",
"pytorch",
"safetensors",
"hubert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T08:10:44+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #hubert #feature-extraction #endpoints_compatible #region-us
| # mhubert-base
* the checkpoint converted from textless s2st real data
## usage:
result:
## Eval
| [
"# mhubert-base\n* the checkpoint converted from textless s2st real data",
"## usage: \n\n\n\n\n\nresult:",
"## Eval"
] | [
"TAGS\n#transformers #pytorch #safetensors #hubert #feature-extraction #endpoints_compatible #region-us \n",
"# mhubert-base\n* the checkpoint converted from textless s2st real data",
"## usage: \n\n\n\n\n\nresult:",
"## Eval"
] |
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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | aiBoo/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T08:11:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2896
* Bleu: 28.1031
* Gen Len: 34.082
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 78.3 | 78.3 |
| test | 79.5 | 79.4 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-repnum_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T08:20:28+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 78.3, F1macro: 78.3
Set: test, F1micro: 79.5, F1macro: 79.4
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 72.4 | 72.2 |
| test | 72.8 | 72.5 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T08:23:44+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 72.4, F1macro: 72.2
Set: test, F1micro: 72.8, F1macro: 72.5
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers | Unrolled PT and FX weights of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-ctc-earnings22-baseline/tree/main | {} | sanchit-gandhi/wav2vec2-ctc-earnings22-baseline | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T10:44:48+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Unrolled PT and FX weights of URL | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/mic... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["food101"], "metrics": ["accuracy"], "model-index": [{"name": "swin-finetuned-food101", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "food101", "type": "food101", "args": "default"}, ... | skylord/swin-finetuned-food101 | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T10:57:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-finetuned-food101
======================
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the food101 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2779
* Accuracy: 0.9214
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
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. -->
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main... | {"tags": ["generated_from_trainer"], "datasets": ["orange_sum"], "metrics": ["rouge"], "model-index": [{"name": "bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "orange_sum", "type": "oran... | Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:orange_sum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T11:27:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-orange_sum #model-index #autotrain_compatible #endpoints_compatible #region-us
| <img src="URL alt="Map of positive probabilities per country." width="200"/>
bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange\_sum
=====================================================
This model is a fine-tuned version of Chemsseddine/bert2gpt2SUMM-finetuned-mlsum on the orange\_sum dataset.
It achieves the follow... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #dataset-orange_sum #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-classification | transformers |
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 75.4 | 75.4 |
| test | 76.1 | 76.0 | | {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-repnum_wl-rua_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T11:35:24+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us
| Eval results
------------
We obtain the following results on and sets:
Set: validation, F1micro: 75.4, F1macro: 75.4
Set: test, F1micro: 76.1, F1macro: 76.0
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Centipede-v4**
This is a trained model of a **PPO** agent playing **Centipede-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 for Stable Baselines3
reinfo... | {"library_name": "stable-baselines3", "tags": ["Centipede-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Centipede-v4", "type": "Centipede-... | Corianas/ppo-Centipedev4 | null | [
"stable-baselines3",
"Centipede-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T11:52:15+00:00 | [] | [] | TAGS
#stable-baselines3 #Centipede-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Centipede-v4
This is a trained model of a PPO agent playing Centipede-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 included.
## Usage (with SB3... | [
"# PPO Agent playing Centipede-v4\nThis is a trained model of a PPO agent playing Centipede-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 agents included.",
"## ... | [
"TAGS\n#stable-baselines3 #Centipede-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Centipede-v4\nThis is a trained model of a PPO agent playing Centipede-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for S... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-squadshifts-vanilla-amazon-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-large-squadshifts-vanilla-amazon-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_squadshifts",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T12:41:47+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-squadshifts-vanilla-amazon-qg'
======================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_squadshifts (dataset\_name: amazon) via 'lmqg'.
### Overview
* Language model: t5-... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (amazon)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTrain... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_squadshifts #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Tr... |
image-classification | fastai |
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"license": "mit", "tags": ["fastai", "image-classification"]} | kurianbenoy/paddy_convnext_model | null | [
"fastai",
"image-classification",
"license:mit",
"has_space",
"region:us"
] | null | 2022-06-20T12:42:41+00:00 | [] | [] | TAGS
#fastai #image-classification #license-mit #has_space #region-us
|
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| [
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
"TAGS\n#fastai #image-classification #license-mit #has_space #region-us \n",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
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. -->
# lmchion/distilbert-finetuned-esg-a4s
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lmchion/distilbert-finetuned-esg-a4s", "results": []}]} | lmchion/distilbert-finetuned-esg-a4s | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T12:45:02+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| lmchion/distilbert-finetuned-esg-a4s
====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.2859
* Validation Loss: 2.3354
* Epoch: 9
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-parsbert-uncased-finetuned-perQA
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https... | {"tags": ["generated_from_trainer"], "datasets": ["persian_qa"], "model-index": [{"name": "bert-base-parsbert-uncased-finetuned-perQA", "results": []}]} | aminnaghavi/bert-base-parsbert-uncased-finetuned-perQA | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:persian_qa",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T12:56:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-persian_qa #endpoints_compatible #region-us
| bert-base-parsbert-uncased-finetuned-perQA
==========================================
This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the persian\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8648
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-persian_qa #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
image-classification | fastai |
# Resnet34 Pokemon Card Classifier
## Model Description
This is a resnet34 model fine-tuned with fastai to [classify real and fake Pokemon cards (dataset)](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
Here is a colab notebook that shows how the model was trained and pushed to the hub: [l... | {"license": ["cc0-1.0"], "tags": ["fastai", "resnet", "computer-vision", "classification", "image-classification", "binary-classification"]} | hugginglearners/pokemon-card-checker | null | [
"fastai",
"resnet",
"computer-vision",
"classification",
"image-classification",
"binary-classification",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-06-20T13:02:07+00:00 | [] | [] | TAGS
#fastai #resnet #computer-vision #classification #image-classification #binary-classification #license-cc0-1.0 #has_space #region-us
|
# Resnet34 Pokemon Card Classifier
## Model Description
This is a resnet34 model fine-tuned with fastai to classify real and fake Pokemon cards (dataset).
Here is a colab notebook that shows how the model was trained and pushed to the hub: link.
## Intended uses & limitation
This model is trained to identify real... | [
"# Resnet34 Pokemon Card Classifier",
"## Model Description\n\nThis is a resnet34 model fine-tuned with fastai to classify real and fake Pokemon cards (dataset).\n\nHere is a colab notebook that shows how the model was trained and pushed to the hub: link.",
"## Intended uses & limitation\n\nThis model is traine... | [
"TAGS\n#fastai #resnet #computer-vision #classification #image-classification #binary-classification #license-cc0-1.0 #has_space #region-us \n",
"# Resnet34 Pokemon Card Classifier",
"## Model Description\n\nThis is a resnet34 model fine-tuned with fastai to classify real and fake Pokemon cards (dataset).\n\nHe... |
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. -->
# philschmid/habana-xlm-r-large-amazon-massive
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xl... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "habana"], "datasets": ["AmazonScience/massive"], "metrics": ["accuracy", "f1"]} | philschmid/habana-xlm-r-large-amazon-massive | null | [
"transformers",
"pytorch",
"tensorboard",
"optimum_habana",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"habana",
"dataset:AmazonScience/massive",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T13:16:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #optimum_habana #xlm-roberta #text-classification #generated_from_trainer #habana #dataset-AmazonScience/massive #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# philschmid/habana-xlm-r-large-amazon-massive
This model is a fine-tuned version of xlm-roberta-large on the AmazonScience/massive dataset.
It achieves the following results on the evaluation set:
## 8x HPU approx. 41min
train results
total
eval results
# Environment
The training was run on a 'DL1' in... | [
"# philschmid/habana-xlm-r-large-amazon-massive\n\nThis model is a fine-tuned version of xlm-roberta-large on the AmazonScience/massive dataset.\nIt achieves the following results on the evaluation set:",
"## 8x HPU approx. 41min\n\ntrain results\n\n\n\ntotal\n\n\n\neval results",
"# Environment\n\nThe training... | [
"TAGS\n#transformers #pytorch #tensorboard #optimum_habana #xlm-roberta #text-classification #generated_from_trainer #habana #dataset-AmazonScience/massive #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# philschmid/habana-xlm-r-large-amazon-massive\n\nThis model is a fine-tuned ... |
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. -->
# lmchion/bert-base-finetuned-esg-a4s
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lmchion/bert-base-finetuned-esg-a4s", "results": []}]} | lmchion/bert-base-finetuned-esg-a4s | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T13:31:53+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| lmchion/bert-base-finetuned-esg-a4s
===================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7744
* Validation Loss: 2.5318
* Epoch: 0
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
translation | transformers |
## [t5-small](https://huggingface.co/t5-small) exported to the ONNX format
## Model description
[T5](https://huggingface.co/docs/transformers/model_doc/t5#t5) is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text ... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | optimum/t5-small | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:... | null | 2022-06-20T13:47:38+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | TAGS
#transformers #onnx #t5 #text2text-generation #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
## t5-small exported to the ONNX format
## Model description
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
For more information, please take a look at the original paper.
Paper: Exploring the ... | [
"## t5-small exported to the ONNX format",
"## Model description\n\nT5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.\n\nFor more information, please take a look at the original paper.\n\nPaper: ... | [
"TAGS\n#transformers #onnx #t5 #text2text-generation #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## t5-small exported to the ONNX format",
"## Model ... |
text-classification | transformers |
# Overview
**ABILaBERT** was created for the purpose of classifying a text to one or more concepts of a *Taxonomy* describing the banking domain. The taxonomy can be bank specific, or general to the domain knowledge: it will be modeled for the text classifier through a pre-training process acting over the Taxonomy its... | {"language": "it", "license": "other", "tags": ["banks", "taxonomy"], "datasets": ["ABILab"], "widget": [{"text": "Processo di gestione del piano di budget attraverso l'individuazione delle regole di predisposizione, la predisposizione effettiva e il controllo del suo rispetto \u00e8 una istanza positiva per il termine... | Abilab-Uniroma2/ABILaBERT | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"banks",
"taxonomy",
"it",
"dataset:ABILab",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T14:00:18+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #camembert #text-classification #banks #taxonomy #it #dataset-ABILab #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# Overview
ABILaBERT was created for the purpose of classifying a text to one or more concepts of a *Taxonomy* describing the banking domain. The taxonomy can be bank specific, or general to the domain knowledge: it will be modeled for the text classifier through a pre-training process acting over the Taxonomy itself.... | [
"# Overview\nABILaBERT was created for the purpose of classifying a text to one or more concepts of a *Taxonomy* describing the banking domain. The taxonomy can be bank specific, or general to the domain knowledge: it will be modeled for the text classifier through a pre-training process acting over the Taxonomy it... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #banks #taxonomy #it #dataset-ABILab #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# Overview\nABILaBERT was created for the purpose of classifying a text to one or more concepts of a *Taxonomy* describing the banking doma... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-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 for Stab... | {"library_name": "stable-baselines3", "tags": ["BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4",... | kingabzpro/dnq-BreakoutNoFrameskip-v4 | null | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T14:00:58+00:00 | [] | [] | TAGS
#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BreakoutNoFrameskip-v4
This is a trained model of a DQN agent playing BreakoutNoFrameskip-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 included.... | [
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-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 agent... | [
"TAGS\n#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a DQN agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
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... | kingabzpro/dnq-SpaceInvadersNoFrameskip-V4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T14:03:53+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... |
text2text-generation | transformers |
# Model Card of `research-backup/t5-large-subjqa-vanilla-books-qg`
This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-quest... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_subjqa"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, starring... | research-backup/t5-large-subjqa-vanilla-books-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question generation",
"en",
"dataset:lmqg/qg_subjqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T14:10:28+00:00 | [
"2210.03992"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card of 'research-backup/t5-large-subjqa-vanilla-books-qg'
================================================================
This model is fine-tuned version of t5-large for question generation task on the lmqg/qg\_subjqa (dataset\_name: books) via 'lmqg'.
### Overview
* Language model: t5-large
* Language: ... | [
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Training data: lmqg/qg\\_subjqa (books)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\n\nTraining hy... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question generation #en #dataset-lmqg/qg_subjqa #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Overview\n\n\n* Language model: t5-large\n* Language: en\n* Trainin... |
null | null | # poetry-generation-nextline-mbart-all-fi-multi
* `nextline`: generates a poem line from previous line(s)
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `all`: trained on data from Project Gutenberg, Wikisource, Poesia publishing house
* `fi`: Finnish langu... | {} | varie/poetry-generation-nextline-mbart-all-fi-multi | null | [
"pytorch",
"region:us"
] | null | 2022-06-20T14:21:14+00:00 | [] | [] | TAGS
#pytorch #region-us
| # poetry-generation-nextline-mbart-all-fi-multi
* 'nextline': generates a poem line from previous line(s)
* 'mbart': base model is facebook/mbart-large-cc25
* 'all': trained on data from Project Gutenberg, Wikisource, Poesia publishing house
* 'fi': Finnish language
* 'multi': uses first, second, and third last l... | [
"# poetry-generation-nextline-mbart-all-fi-multi\n\n * 'nextline': generates a poem line from previous line(s)\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'all': trained on data from Project Gutenberg, Wikisource, Poesia publishing house\n * 'fi': Finnish language\n * 'multi': uses first, second, and t... | [
"TAGS\n#pytorch #region-us \n",
"# poetry-generation-nextline-mbart-all-fi-multi\n\n * 'nextline': generates a poem line from previous line(s)\n * 'mbart': base model is facebook/mbart-large-cc25\n * 'all': trained on data from Project Gutenberg, Wikisource, Poesia publishing house\n * 'fi': Finnish language\n * ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-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... | furyhawk/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T14:27:50+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-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: 0.1360
* F1: 0.8654
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
fill-mask | transformers | **Please use 'Bert' related tokenizer classes and 'Nezha' related model classes**
[NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204)
Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.... | {"license": "afl-3.0"} | sijunhe/nezha-large-wwm | null | [
"transformers",
"pytorch",
"nezha",
"fill-mask",
"arxiv:1909.00204",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T14:58:37+00:00 | [
"1909.00204"
] | [] | TAGS
#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Please use 'Bert' related tokenizer classes and 'Nezha' related model classes
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
The original checkpoints can be found ... | [
"## Example Usage"
] | [
"TAGS\n#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Example Usage"
] |
fill-mask | transformers | **Please use 'Bert' related tokenizer classes and 'Nezha' related model classes**
[NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204)
Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.... | {"license": "afl-3.0"} | sijunhe/nezha-cn-large | null | [
"transformers",
"pytorch",
"nezha",
"fill-mask",
"arxiv:1909.00204",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T15:05:41+00:00 | [
"1909.00204"
] | [] | TAGS
#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Please use 'Bert' related tokenizer classes and 'Nezha' related model classes
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
The original checkpoints can be found ... | [
"## Example Usage"
] | [
"TAGS\n#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Example Usage"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826
## Validation Metrics
- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.867984655464... | {"language": "en", "tags": "autotrain", "datasets": ["Siddish/autotrain-data-yes-or-no-classifier-on-circa"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.1287915253247826} | Siddish/autotrain-yes-or-no-classifier-on-circa-1009033469 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:Siddish/autotrain-data-yes-or-no-classifier-on-circa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T15:06:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Siddish/autotrain-data-yes-or-no-classifier-on-circa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826
## Validation Metrics
- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.867984655464... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1009033469\n- CO2 Emissions (in grams): 0.1287915253247826",
"## Validation Metrics\n\n- Loss: 0.4084862470626831\n- Accuracy: 0.8722054859679721\n- Macro F1: 0.6340608446004876\n- Micro F1: 0.8722054859679722\n- Weighted ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Siddish/autotrain-data-yes-or-no-classifier-on-circa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1009033... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamW', 'learning_rate':... | {"library_name": "keras"} | martin-ha/text_image_dual_encoder | null | [
"keras",
"region:us"
] | null | 2022-06-20T15:19:19+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamW', 'learning_rate':... | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- optimizer: {'nam... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used duri... |
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/1449034383420182531/Ava9... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dougjballoon/1655742171463/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dougjballoon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T15:22:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
New York Times Pitchbot
@dougjballoon
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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="ubiqtuitin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional a... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | ubiqtuitin/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-20T15:23:31+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"
] |
token-classification | transformers |
# Table of Contents
- [Model description](#model-description)
- [Training procedure](#training-procedure)
- [Dataset](#dataset)
- [Results](#results)
- [Limitations and biases](#limitations-and-biases)
- [BibTeX entry and citation info](#bibtex-entry-and-citation-info)
# Model description
**mbert-base-cased-NE... | {"language": ["nl"], "license": ["mit"], "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["romjansen/mbert-base-cased-NER-NL-legislation-refs-data"], "metrics": ["seqeval"], "widget": [{"text": "5.2. De rechtbank overweegt dat het voor buiten het Koninkrijk geboren Nederlanders, die door... | romjansen/mbert-base-cased-NER-NL-legislation-refs | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"nl",
"dataset:romjansen/mbert-base-cased-NER-NL-legislation-refs-data",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T15:25:36+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #nl #dataset-romjansen/mbert-base-cased-NER-NL-legislation-refs-data #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Table of Contents
=================
* Model description
* Training procedure
+ Dataset
+ Results
+ Limitations and biases
* BibTeX entry and citation info
Model description
=================
mbert-base-cased-NER-NL-legislation-refs is a fine-tuned BERT model that was trained to recognize the entity type 'legis... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #nl #dataset-romjansen/mbert-base-cased-NER-NL-legislation-refs-data #license-mit #model-index #autotrain_compatible #endpoints_compatible #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="ubiqtuitin/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": "7.54 +/... | ubiqtuitin/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-20T15:25:59+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
object-detection | stable-baselines3 |
# YOLOv5
Ultralytics YOLOv5 model in Pytorch.
Proof of concept for (TypoSquatting, Niche Squatting) security flaw on Hugging Face.
## Model Description
## How to use
```python
from transformers import YolosFeatureExtractor, YolosForObjectDetection
from PIL import Image
import requests
url = 'http://images.... | {"license": "gpl-2.0", "library_name": "stable-baselines3", "tags": ["seals/CartPole-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3", "object-detection"], "datasets": ["coco"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcemen... | mhyatt000/YOLOv5 | null | [
"stable-baselines3",
"seals/CartPole-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"object-detection",
"dataset:coco",
"license:gpl-2.0",
"model-index",
"region:us"
] | null | 2022-06-20T15:37:08+00:00 | [] | [] | TAGS
#stable-baselines3 #seals/CartPole-v0 #deep-reinforcement-learning #reinforcement-learning #object-detection #dataset-coco #license-gpl-2.0 #model-index #region-us
| YOLOv5
======
Ultralytics YOLOv5 model in Pytorch.
Proof of concept for (TypoSquatting, Niche Squatting) security flaw on Hugging Face.
Model Description
-----------------
How to use
----------
Training Data
-------------
### Training
Evaluation
----------
Model was evaluated on COCO2017 dataset.
###... | [
"### Training\n\n\nEvaluation\n----------\n\n\nModel was evaluated on COCO2017 dataset.",
"### Bibtex and citation info"
] | [
"TAGS\n#stable-baselines3 #seals/CartPole-v0 #deep-reinforcement-learning #reinforcement-learning #object-detection #dataset-coco #license-gpl-2.0 #model-index #region-us \n",
"### Training\n\n\nEvaluation\n----------\n\n\nModel was evaluated on COCO2017 dataset.",
"### Bibtex and citation info"
] |
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... | Gerard/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 | 2022-06-20T15:51:28+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\\_... |
token-classification | transformers |
# Table of Contents
- [Model description](#model-description)
- [Training procedure](#training-procedure)
- [Dataset](#dataset)
- [Results](#results)
- [Limitations and biases](#limitations-and-biases)
- [BibTeX entry and citation info](#bibtex-entry-and-citation-info)
# Model description
**robbert-base-v2-NER... | {"language": ["nl"], "license": ["mit"], "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["romjansen/robbert-base-v2-NER-NL-legislation-refs-data"], "metrics": ["seqeval"], "widget": [{"text": "5.2. De rechtbank overweegt dat het voor buiten het Koninkrijk geboren Nederlanders, die door ... | romjansen/robbert-base-v2-NER-NL-legislation-refs | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"nl",
"dataset:romjansen/robbert-base-v2-NER-NL-legislation-refs-data",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T15:55:19+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #nl #dataset-romjansen/robbert-base-v2-NER-NL-legislation-refs-data #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Table of Contents
=================
* Model description
* Training procedure
+ Dataset
+ Results
+ Limitations and biases
* BibTeX entry and citation info
Model description
=================
robbert-base-v2-NER-NL-legislation-refs is a fine-tuned RobBERT model that was trained to recognize the entity type 'leg... | [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #nl #dataset-romjansen/robbert-base-v2-NER-NL-legislation-refs-data #license-mit #model-index #autotrain_compatible #endpoints_compatible #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. -->
# prompt-tuned-t5-large-num-tokens-100-squad
This model is a fine-tuned version of [google/t5-large-lm-adapt](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "prompt-tuned-t5-large-num-tokens-100-squad", "results": []}]} | anas-awadalla/prompt-tuned-t5-large-num-tokens-100-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T16:09:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# prompt-tuned-t5-large-num-tokens-100-squad
This model is a fine-tuned version of google/t5-large-lm-adapt on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# prompt-tuned-t5-large-num-tokens-100-squad\n\nThis model is a fine-tuned version of google/t5-large-lm-adapt on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# prompt-tuned-t5-large-num-tokens-100-squad\n\nThis model is a fine-tuned version of google/t5-large-lm-adapt on the squad dataset.",
"## Model description\n\nMore inform... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **PPO** agent playing **MountainCarContinuous-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
f... | {"library_name": "stable-baselines3", "tags": ["MountainCarContinuous-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCarContinuous-... | danieladejumo/ppo-mountan_car | null | [
"stable-baselines3",
"MountainCarContinuous-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T16:10:10+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCarContinuous-v0
This is a trained model of a PPO agent playing MountainCarContinuous-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a PPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library.",
"## Usage (with Sta... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | biwako/test1MountainCar | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T16:13:07+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\nThis is a trained model of a PPO agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | biwako/test7LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T16:28:32+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 |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Evokus/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T16:29:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
question-answering | transformers |
# EnViT5-base
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese and English used in [MTet's paper](https://arxiv.org/abs/2210.05610).
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/mtet).
[Finetunning examples can be found here](https://git... | {"language": "vi", "license": "mit", "tags": ["summarization", "translation", "question-answering"], "datasets": ["cc100"]} | VietAI/envit5-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"summarization",
"translation",
"question-answering",
"vi",
"dataset:cc100",
"arxiv:2210.05610",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-20T16:52:40+00:00 | [
"2210.05610"
] | [
"vi"
] | TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #translation #question-answering #vi #dataset-cc100 #arxiv-2210.05610 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# EnViT5-base
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese and English used in MTet's paper.
## How to use
For more details, do check out our Github repo.
Finetunning examples can be found here.
| [
"# EnViT5-base\n\nState-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese and English used in MTet's paper.",
"## How to use\nFor more details, do check out our Github repo. \n\nFinetunning examples can be found here."
] | [
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #translation #question-answering #vi #dataset-cc100 #arxiv-2210.05610 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# EnViT5-base\n\nState-of-the-art pretrained Transformer-based ... |
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... | GauthamB/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-20T16:56:52+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
null | null |
Some brief description of the Grad-TTS model will soon arrive here. | {"license": "other"} | huawei-noah/Grad-TTS | null | [
"license:other",
"region:us"
] | null | 2022-06-20T17:06:15+00:00 | [] | [] | TAGS
#license-other #region-us
|
Some brief description of the Grad-TTS model will soon arrive here. | [] | [
"TAGS\n#license-other #region-us \n"
] |
fill-mask | transformers |
# PolitiBETO: A Spanish BERT adapted to a language domain of Political Tweets
PolitiBETO is a [BERT model](https://github.com/google-research/bert) tailored for political tasks in social media corpora. It is a Domain Adaptation on top of [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased), a pretra... | {"language": ["es"], "tags": ["masked-lm"], "widget": [{"text": "La mayor ventaja de la democracia es su [MASK].", "example_title": "Ejemplo 1"}]} | nlp-cimat/politibeto | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"masked-lm",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T17:09:39+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #fill-mask #masked-lm #es #autotrain_compatible #endpoints_compatible #region-us
|
# PolitiBETO: A Spanish BERT adapted to a language domain of Political Tweets
PolitiBETO is a BERT model tailored for political tasks in social media corpora. It is a Domain Adaptation on top of BETO, a pretrained BERT in Spanish.
This model is meant to be fine-tuned for downstream tasks.
NLP-CIMAT at PoliticEs 202... | [
"# PolitiBETO: A Spanish BERT adapted to a language domain of Political Tweets\n\nPolitiBETO is a BERT model tailored for political tasks in social media corpora. It is a Domain Adaptation on top of BETO, a pretrained BERT in Spanish.\nThis model is meant to be fine-tuned for downstream tasks.\n\n\nNLP-CIMAT at Pol... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #masked-lm #es #autotrain_compatible #endpoints_compatible #region-us \n",
"# PolitiBETO: A Spanish BERT adapted to a language domain of Political Tweets\n\nPolitiBETO is a BERT model tailored for political tasks in social media corpora. It is a Domain Adaptation on ... |
null | keras |
# Vector-Quantized Variational Autoencoders (VQ-VAE)
## Model description
Learning latent space representations of data remains to be an important task in machine learning. This model, the Vector-Quantized Variational Autoencoder (VQ-VAE) builds upon traditional VAEs in two ways.
- The encoder network outputs discret... | {"library_name": "keras", "tags": ["feature extraction", "autoencoder"]} | keras-io/vq-vae | null | [
"keras",
"tensorboard",
"feature extraction",
"autoencoder",
"arxiv:1711.00937",
"region:us"
] | null | 2022-06-20T17:38:49+00:00 | [
"1711.00937"
] | [] | TAGS
#keras #tensorboard #feature extraction #autoencoder #arxiv-1711.00937 #region-us
|
# Vector-Quantized Variational Autoencoders (VQ-VAE)
## Model description
Learning latent space representations of data remains to be an important task in machine learning. This model, the Vector-Quantized Variational Autoencoder (VQ-VAE) builds upon traditional VAEs in two ways.
- The encoder network outputs discret... | [
"# Vector-Quantized Variational Autoencoders (VQ-VAE)",
"## Model description\nLearning latent space representations of data remains to be an important task in machine learning. This model, the Vector-Quantized Variational Autoencoder (VQ-VAE) builds upon traditional VAEs in two ways.\n- The encoder network outpu... | [
"TAGS\n#keras #tensorboard #feature extraction #autoencoder #arxiv-1711.00937 #region-us \n",
"# Vector-Quantized Variational Autoencoders (VQ-VAE)",
"## Model description\nLearning latent space representations of data remains to be an important task in machine learning. This model, the Vector-Quantized Variati... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielens-user-model | null | [
"keras",
"region:us"
] | null | 2022-06-20T17:47:11+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | ashraq/movielens-movie-model | null | [
"keras",
"region:us"
] | null | 2022-06-20T17:55:45+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | ornil1/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T18:12:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
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