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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="TinySuitStarfish/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add addition... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | TinySuitStarfish/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-15T09:09:31+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | rajendra-ml/Chandrayaan | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T09:16:00+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 | This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Yesenin's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in Yesenin's style.
![alt text](https://lh3.googleusercontent.com/GFvLjpEgChuXAalHquE3zl22Cqx7ipO233p... | {} | AnyaSchen/rugpt3_esenin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T09:28:01+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Yesenin's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in Yesenin's style.
!alt text | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #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... | fabiochiu/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T09:32:10+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-mya
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["mya", "my"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-mya | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"mya",
"my",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T09:36:51+00:00 | [] | [
"mya",
"my"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #mya #my #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-mya
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - MYA (Burme... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #mya #my #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | mikeluck/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T09:44:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5351
* Wer: 0.3384
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
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. -->
# test2
This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224... | {"tags": ["generated_from_trainer"], "base_model": "flyswot/convnext-tiny-224_flyswot", "model-index": [{"name": "test2", "results": []}]} | flyswot/test2 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:flyswot/convnext-tiny-224_flyswot",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T09:46:33+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #base_model-flyswot/convnext-tiny-224_flyswot #autotrain_compatible #endpoints_compatible #region-us
| test2
=====
This model is a fine-tuned version of flyswot/convnext-tiny-224\_flyswot on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #base_model-flyswot/convnext-tiny-224_flyswot #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* tr... |
null | 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"]} | rajeshradhakrishnan/ml-news-classify-fastai | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-06-15T09:53:22+00:00 | [] | [] | TAGS
#fastai #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 its... | [
"# 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 #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 @waydegilliam for blurr\n\n മലയാളത്തിൽ മെഷീൻ ലീർണിങ് പഠിക്കാനും ... |
text-generation | transformers |
# Rem DialoGPT Model | {"tags": ["conversational"]} | Fluffypillow/DialoGPT-small-Rem | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T10:02:49+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rem DialoGPT Model | [
"# Rem DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rem DialoGPT Model"
] |
text-generation | transformers |
#Hermite DialoGPT Model | {"tags": "conversational"} | Hermite/DialoGPT-large-hermite2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T10:08:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Hermite DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Blok's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in Blok's style.
![alt text](https://lh4.googleusercontent.com/BxsTIgzhQesSrRY-7erc7S3fFOQxkj1sXEnnYN-6P... | {} | AnyaSchen/rugpt3_blok | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T10:20:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Blok's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in Blok's style.
!alt text | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Tyutchev's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in the style of Tyutchev.
![alt text](https://lh4.googleusercontent.com/1B05-wqyj_8gI6zTues5f7a1epqk... | {} | AnyaSchen/rugpt3_tyutchev | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T10:27:40+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Tyutchev's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in the style of Tyutchev.
!alt text | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | fill it later | {} | MaksMaib/PetGradioStyleTransfer | null | [
"region:us"
] | null | 2022-06-15T10:29:14+00:00 | [] | [] | TAGS
#region-us
| fill it later | [] | [
"TAGS\n#region-us \n"
] |
null | null | https://www.beesource.com/members/magelang1337.142760/#about
https://leasedadspace.com/frame.php?bfm_page=members/magelang1337&aid=magelang1337
https://www.jqwidgets.com/community/users/magelang1337/
https://metalstorm.net/users/magelang1337/profile
https://myanimelist.net/profile/mnhblog
https://forum.codeigniter.com/... | {} | magelang1337/Backlinks | null | [
"region:us"
] | null | 2022-06-15T10:56:30+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Sanjeev49/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Hel... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Sanjeev49/marian-finetuned-kde4-en-to-fr", "results": []}]} | Sanjeev49/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T11:07:09+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Sanjeev49/marian-finetuned-kde4-en-to-fr
========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0601
* Validation Loss: 0.8952
* Epoch: 0
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 5912, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #marian #text2text-generation #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\... |
image-classification | transformers |
# Swin Transformer v2 (small-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mic... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-small-patch4-window8-256 | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T11:20:12+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (small-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did no... | [
"# Swin Transformer v2 (small-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (small-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It w... |
fill-mask | transformers |
# mBERT swedish distilled base model (cased)
This model is a distilled version of [mBERT](https://huggingface.co/bert-base-multilingual-cased). It was distilled using Swedish data, the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The code for the di... | {"language": ["multilingual", "sv"], "license": "apache-2.0", "datasets": "KBLab/sucx3_ner"} | Addedk/mbert-swedish-distilled-cased | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"sv",
"dataset:KBLab/sucx3_ner",
"arxiv:2103.06418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T11:22:20+00:00 | [
"2103.06418"
] | [
"multilingual",
"sv"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #sv #dataset-KBLab/sucx3_ner #arxiv-2103.06418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# mBERT swedish distilled base model (cased)
This model is a distilled version of mBERT. It was distilled using Swedish data, the 2010-2015 portion of the Swedish Culturomics Gigaword Corpus. The code for the distillation process can be found here. This was done as part of my Master's Thesis: *Task-agnostic knowledge... | [
"# mBERT swedish distilled base model (cased)\n\nThis model is a distilled version of mBERT. It was distilled using Swedish data, the 2010-2015 portion of the Swedish Culturomics Gigaword Corpus. The code for the distillation process can be found here. This was done as part of my Master's Thesis: *Task-agnostic kno... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #multilingual #sv #dataset-KBLab/sucx3_ner #arxiv-2103.06418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBERT swedish distilled base model (cased)\n\nThis model is a distilled version of mBERT. It was distilled... |
image-classification | transformers |
# Swin Transformer v2 (small-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mic... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-small-patch4-window16-256 | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-15T11:28:05+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer v2 (small-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did no... | [
"# Swin Transformer v2 (small-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer v2 (small-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 25... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-snk
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["snk"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-snk | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"snk",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T11:33:08+00:00 | [] | [
"snk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #snk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-snk
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - SNK (Sonin... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #snk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
image-classification | transformers |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-base-patch4-window8-256 | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-15T11:35:14+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did not... | [
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v2... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256... |
image-classification | transformers |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/micr... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-base-patch4-window16-256 | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T11:38:59+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did not... | [
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v2... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It wa... |
image-classification | transformers |
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mic... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-base-patch4-window12-192-22k | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T11:41:50+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did no... | [
"# Swin Transformer v2 (tiny-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer v... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (tiny-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It w... |
image-classification | transformers |
# Swin Transformer v2 (large-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/mi... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-large-patch4-window12-192-22k | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-15T11:47:41+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Swin Transformer v2 (large-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releasing Swin Transformer v2 did n... | [
"# Swin Transformer v2 (large-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The team releasing Swin Transformer ... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Swin Transformer v2 (large-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k at resolution 1... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 985232782
- CO2 Emissions (in grams): 894.9753853627794
## Validation Metrics
- Loss: 1.9692628383636475
- Rouge1: 19.3642
- Rouge2: 7.3644
- RougeL: 16.148
- RougeLsum: 16.4988
- Gen Len: 18.9975
## Usage
You can use cURL to access this mo... | {"language": "fr", "tags": "autotrain", "datasets": ["ouiame/autotrain-data-trainproject"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 894.9753853627794} | ouiame/bert2gpt2Summy | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"fr",
"dataset:ouiame/autotrain-data-trainproject",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T12:08:46+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #fr #dataset-ouiame/autotrain-data-trainproject #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 985232782
- CO2 Emissions (in grams): 894.9753853627794
## Validation Metrics
- Loss: 1.9692628383636475
- Rouge1: 19.3642
- Rouge2: 7.3644
- RougeL: 16.148
- RougeLsum: 16.4988
- Gen Len: 18.9975
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 985232782\n- CO2 Emissions (in grams): 894.9753853627794",
"## Validation Metrics\n\n- Loss: 1.9692628383636475\n- Rouge1: 19.3642\n- Rouge2: 7.3644\n- RougeL: 16.148\n- RougeLsum: 16.4988\n- Gen Len: 18.9975",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #fr #dataset-ouiame/autotrain-data-trainproject #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 985232782\n- C... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-myk
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["myk"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-myk | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"myk",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T12:27:46+00:00 | [] | [
"myk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #myk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-myk
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - MYK (Sénou... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #myk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 985232789
- CO2 Emissions (in grams): 976.8219757938544
## Validation Metrics
- Loss: 1.7047555446624756
- Rouge1: 20.2108
- Rouge2: 7.8633
- RougeL: 16.9554
- RougeLsum: 17.3178
- Gen Len: 18.9874
## Usage
You can use cURL to access this m... | {"language": "fr", "tags": "autotrain", "datasets": ["ouiame/autotrain-data-trainproject"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 976.8219757938544} | ouiame/T5_mlsum | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"fr",
"dataset:ouiame/autotrain-data-trainproject",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T12:51:07+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain #fr #dataset-ouiame/autotrain-data-trainproject #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 985232789
- CO2 Emissions (in grams): 976.8219757938544
## Validation Metrics
- Loss: 1.7047555446624756
- Rouge1: 20.2108
- Rouge2: 7.8633
- RougeL: 16.9554
- RougeLsum: 17.3178
- Gen Len: 18.9874
## Usage
You can use cURL to access this m... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 985232789\n- CO2 Emissions (in grams): 976.8219757938544",
"## Validation Metrics\n\n- Loss: 1.7047555446624756\n- Rouge1: 20.2108\n- Rouge2: 7.8633\n- RougeL: 16.9554\n- RougeLsum: 17.3178\n- Gen Len: 18.9874",
"## Usage\n\nYou can ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain #fr #dataset-ouiame/autotrain-data-trainproject #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 985232789\n- C... |
text-generation | transformers |
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"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T13:43:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
asad abukhalil أسعد أبو خليل
@asadabukhalil
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.
Tra... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-spa
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["spa", "es"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-spa | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"spa",
"es",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T14:03:29+00:00 | [] | [
"spa",
"es"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #spa #es #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-spa
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - SPA (Spani... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #spa #es #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# stsb
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "model-index": [{"name": "stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE STSB", "type": "glue", "args": "stsb"}, "metri... | Alireza1044/mobilebert_stsb | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T14:05:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# stsb
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5348
- Pearson: 0.8773
- Spearmanr: 0.8735
- Combined Score: 0.8754
## Model description
More information needed
## Intended uses & limitations
More... | [
"# stsb\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE STSB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5348\n- Pearson: 0.8773\n- Spearmanr: 0.8735\n- Combined Score: 0.8754",
"## Model description\n\nMore information needed",
"## Intended uses ... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# stsb\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE STSB dataset.\nI... |
fill-mask | transformers | # Pre-trained Language Model for the Humanities and Social Sciences in Chinese
## Introduction
The research for social science texts in Chinese needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is... | {"license": "apache-2.0"} | KM4STfulltext/CSSCI_ABS_roberta | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T14:22:39+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Pre-trained Language Model for the Humanities and Social Sciences in Chinese
============================================================================
Introduction
------------
The research for social science texts in Chinese needs the support natural language processing tools.
The pre-trained language model h... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain CSSCI\\_ABS\\_BERT, CSSCI\\_ABS\\_roberta and CSSCI\\_ABS\\_roberta-wwm models online.\n\n\n* CSSCI\\_ABS\\_BERT\n* CSSCI\\_ABS\\_roberta\n* CSSCI\\_ABS\\_roberta-wwm",
"### Download Models\n\n\... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain CSSCI\\_ABS\\_BERT, CSSCI\\_ABS\\_roberta and CSSCI\\_ABS\\_roberta... |
fill-mask | transformers | # Pre-trained Language Model for the Humanities and Social Sciences in Chinese
## Introduction
The research for social science texts in Chinese needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is... | {"license": "apache-2.0"} | KM4STfulltext/CSSCI_ABS_BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T14:33:23+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Pre-trained Language Model for the Humanities and Social Sciences in Chinese
============================================================================
Introduction
------------
The research for social science texts in Chinese needs the support natural language processing tools.
The pre-trained language model h... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain CSSCI\\_ABS\\_BERT, CSSCI\\_ABS\\_roberta and CSSCI\\_ABS\\_roberta-wwm models online.\n\n\n* CSSCI\\_ABS\\_BERT\n* CSSCI\\_ABS\\_roberta\n* CSSCI\\_ABS\\_roberta-wwm",
"### Download Models\n\n\... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain CSSCI\\_ABS\\_BERT, CSSCI\\_ABS\\_roberta and CSSCI\\_ABS\\_roberta... |
fill-mask | transformers | # Pre-trained Language Model for the Humanities and Social Sciences in Chinese
## Introduction
The research for social science texts in Chinese needs the support natural language processing tools.
The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is... | {"license": "apache-2.0"} | KM4STfulltext/CSSCI_ABS_roberta_wwm | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T14:33:54+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Pre-trained Language Model for the Humanities and Social Sciences in Chinese
============================================================================
Introduction
------------
The research for social science texts in Chinese needs the support natural language processing tools.
The pre-trained language model h... | [
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain CSSCI\\_ABS\\_BERT, CSSCI\\_ABS\\_roberta and CSSCI\\_ABS\\_roberta-wwm models online.\n\n\n* CSSCI\\_ABS\\_BERT\n* CSSCI\\_ABS\\_roberta\n* CSSCI\\_ABS\\_roberta-wwm",
"### Download Models\n\n\... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Huggingface Transformers\n\n\nThe 'from\\_pretrained' method based on Huggingface Transformers can directly obtain CSSCI\\_ABS\\_BERT, CSSCI\\_ABS\\_roberta and CSSCI\\_ABS\\_roberta... |
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="Guillaume63/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional ... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | Guillaume63/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-15T14:41:07+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"
] |
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. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | Makabaka/bert-base-uncased-EnglishLawAI | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T14:50:49+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5503
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 16\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #bert #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: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
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. -->
# rte
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the G... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "metrics":... | Alireza1044/mobilebert_rte | null | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T15:09:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rte
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8396
- Accuracy: 0.6679
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluatio... | [
"# rte\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE RTE dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8396\n- Accuracy: 0.6679",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #mobilebert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rte\n\nThis model is a fine-tuned version of google/mobilebert-uncased on the GLUE RTE dataset.\nIt ... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-sdk
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["sdk"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["sil-ai/bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL In... | sil-ai/wav2vec2-bloom-speech-sdk | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"sdk",
"dataset:sil-ai/bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T15:18:59+00:00 | [] | [
"sdk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #sdk #dataset-sil-ai/bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-sdk
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech SDK (Sos Kun... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #sdk #dataset-sil-ai/bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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... | SimulSt/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 | 2022-06-15T15:20:48+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\\_... |
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. -->
# xlmroberta2xlmroberta-finetune-summarization-ur
This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum d... | {"tags": ["summarization", "ur", "encoder-decoder", "xlm-roberta", "Abstractive Summarization", "roberta", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "xlmroberta2xlmroberta-finetune-summarization-ur", "results": []}]} | ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ur | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ur",
"xlm-roberta",
"Abstractive Summarization",
"roberta",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T15:34:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ur #xlm-roberta #Abstractive Summarization #roberta #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us
|
# xlmroberta2xlmroberta-finetune-summarization-ur
This model is a fine-tuned version of [](URL on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4576
- Rouge-1: 26.51
- Rouge-2: 9.4
- Rouge-l: 23.21
- Gen Len: 19.99
- Bertscore: 68.15
## Model description
More information ne... | [
"# xlmroberta2xlmroberta-finetune-summarization-ur\n\nThis model is a fine-tuned version of [](URL on the xlsum dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 5.4576\n- Rouge-1: 26.51\n- Rouge-2: 9.4\n- Rouge-l: 23.21\n- Gen Len: 19.99\n- Bertscore: 68.15",
"## Model description\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ur #xlm-roberta #Abstractive Summarization #roberta #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlmroberta2xlmroberta-finetune-summarization-ur\n\nThis model ... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-stk
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["stk"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-stk | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"stk",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T15:48:24+00:00 | [] | [
"stk"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #stk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-stk
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - STK (Aramb... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #stk #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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/1522920330960027648/Z5pi... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/_mohamads/1655314541919/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/_mohamads | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T16:33:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
محمد الزهراني
@\_mohamads
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 |
# Model Card of `lmqg/t5-large-squadshifts-new_wiki-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://gith... | {"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-new_wiki-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 | 2022-06-15T16:58:32+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 'lmqg/t5-large-squadshifts-new\_wiki-qg'
======================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: new\_wiki) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
*... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (new\\_wiki)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric fi... | [
"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: lmqg/t5-large-squad\n* Languag... |
null | keras |
## Model description
This model demonstrates real-valued non-volume preserving (real NVP) transformations, a
tractable yet expressive approach to modeling high-dimensional data.
This model is used to map a simple distribution - which is easy to sample and whose density is simple to estimate - to a more complex one l... | {"library_name": "keras", "tags": ["generative"]} | keras-io/real_nvp | null | [
"keras",
"tensorboard",
"generative",
"region:us"
] | null | 2022-06-15T16:59:36+00:00 | [] | [] | TAGS
#keras #tensorboard #generative #region-us
| Model description
-----------------
This model demonstrates real-valued non-volume preserving (real NVP) transformations, a
tractable yet expressive approach to modeling high-dimensional data.
This model is used to map a simple distribution - which is easy to sample and whose density is simple to estimate - to a mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:"
] | [
"TAGS\n#keras #tensorboard #generative #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:"
] |
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. -->
# CUBERT
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieve... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "CUBERT", "results": []}]} | zluvolyote/CUBERT | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T17:09:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| CUBERT
======
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2203
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n*... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-eng
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["eng", "en"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-eng | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"eng",
"en",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T17:22:11+00:00 | [] | [
"eng",
"en"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #eng #en #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-eng
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - ENG (Engli... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #eng #en #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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... | Ambiwlans/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T17:23:45+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# inquisitive2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "inquisitive2", "results": []}]} | kcarnold/inquisitive2 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T17:28:55+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# inquisitive2
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1760
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More inform... | [
"# inquisitive2\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.1760",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evalua... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# inquisitive2\n\nThis model is a fine-tuned version of facebook/bart-base on an unknown dataset.\nIt achieves the following results on the evaluation ... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-squadshifts-nyt-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asa... | {"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-nyt-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 | 2022-06-15T18:07:27+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 'lmqg/t5-large-squadshifts-nyt-qg'
================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: nyt) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language: en
* Tr... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (nyt)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n\n\... | [
"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: lmqg/t5-large-squad\n* Languag... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 990032813
- CO2 Emissions (in grams): 1.52810485048449
## Validation Metrics
- Loss: 0.7393798828125
- Accuracy: 0.5
- Precision: 0.5
- Recall: 1.0
- AUC: 0.0
- F1: 0.6666666666666666
## Usage
You can use cURL to access this model:
... | {"language": "en", "tags": "autotrain", "datasets": ["liux3790/autotrain-data-journals-covid"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.52810485048449} | liux3790/autotrain-journals-covid-990032813 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:liux3790/autotrain-data-journals-covid",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T18:08:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-liux3790/autotrain-data-journals-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 990032813
- CO2 Emissions (in grams): 1.52810485048449
## Validation Metrics
- Loss: 0.7393798828125
- Accuracy: 0.5
- Precision: 0.5
- Recall: 1.0
- AUC: 0.0
- F1: 0.6666666666666666
## Usage
You can use cURL to access this model:
... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 990032813\n- CO2 Emissions (in grams): 1.52810485048449",
"## Validation Metrics\n\n- Loss: 0.7393798828125\n- Accuracy: 0.5\n- Precision: 0.5\n- Recall: 1.0\n- AUC: 0.0\n- F1: 0.6666666666666666",
"## Usage\n\nYou can use cU... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-liux3790/autotrain-data-journals-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 990032813\n- CO2 Emissions (i... |
automatic-speech-recognition | transformers | # wav2vec2-bloom-speech-tgl
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["tgl", "tl"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Int... | sil-ai/wav2vec2-bloom-speech-tgl | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"tgl",
"tl",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T18:27:23+00:00 | [] | [
"tgl",
"tl"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #tgl #tl #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-tgl
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - TGL (Tagal... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #tgl #tl #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-chd
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["chd"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-chd | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"chd",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T19:04:02+00:00 | [] | [
"chd"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #chd #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-chd
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - CHD (Chont... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #chd #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text2text-generation | transformers |
# Model Card of `lmqg/t5-large-squadshifts-reddit-qg`
This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.c... | {"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squadshifts"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "generate question: <hl> Beyonce <hl> further expanded her acting career, sta... | research-backup/t5-large-squadshifts-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 | 2022-06-15T19:08:32+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 'lmqg/t5-large-squadshifts-reddit-qg'
===================================================
This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg\_squadshifts (dataset\_name: reddit) via 'lmqg'.
### Overview
* Language model: lmqg/t5-large-squad
* Language... | [
"### Overview\n\n\n* Language model: lmqg/t5-large-squad\n* Language: en\n* Training data: lmqg/qg\\_squadshifts (reddit)\n* Online Demo: URL\n* Repository: URL\n* Paper: URL",
"### Usage\n\n\n* With 'lmqg'\n* With 'transformers'\n\n\nEvaluation\n----------\n\n\n* *Metric (Question Generation)*: raw metric file\n... | [
"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: lmqg/t5-large-squad\n* Languag... |
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... | jianyang/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-15T19:30:43+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
automatic-speech-recognition | transformers |
# wav2vec2-bloom-speech-tpi
 
## Model description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI ... | {"language": ["tpi"], "license": "other", "tags": ["automatic-speech-recognition", "sil-ai/bloom-speech", "generated_from_trainer"], "datasets": ["bloom_speech"], "extra_gated_prompt": "One more step before getting this model.\n\nThis model is open access and available only for non-commercial use, with an SIL Internati... | sil-ai/wav2vec2-bloom-speech-tpi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"sil-ai/bloom-speech",
"generated_from_trainer",
"tpi",
"dataset:bloom_speech",
"license:other",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T19:36:13+00:00 | [] | [
"tpi"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #tpi #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us
| wav2vec2-bloom-speech-tpi
=========================
!logo for Bloom Library !sil-ai logo
Model description
-----------------
* Homepage: SIL AI
* Point of Contact: SIL AI email
* Source Data: Bloom Library
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - TPI (Tok P... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #sil-ai/bloom-speech #generated_from_trainer #tpi #dataset-bloom_speech #license-other #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\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/1438226079030947845/pwH4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/yemeen/1655328324400/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/yemeen | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T20:22:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
𝕐𝕖𝕞𝕖𝕖𝕟
@yemeen
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | keras |
## Model description
This is an image classification model based on a [WideResNet-2-28](https://arxiv.org/abs/1605.07146v4), trained using the [AdaMatch](https://arxiv.org/abs/2106.04732) method by Berthelot et al.
The training was based on the example [Semi-supervision and domain adaptation with AdaMatch]('https:... | {"library_name": "keras", "tags": ["semi-supervised", "image classification", "domain adaption"], "datasets": ["MNIST", "SVHN"]} | keras-io/adamatch-domain-adaption | null | [
"keras",
"tensorboard",
"semi-supervised",
"image classification",
"domain adaption",
"dataset:MNIST",
"dataset:SVHN",
"arxiv:1605.07146",
"arxiv:2106.04732",
"has_space",
"region:us"
] | null | 2022-06-15T20:31:22+00:00 | [
"1605.07146",
"2106.04732"
] | [] | TAGS
#keras #tensorboard #semi-supervised #image classification #domain adaption #dataset-MNIST #dataset-SVHN #arxiv-1605.07146 #arxiv-2106.04732 #has_space #region-us
|
## Model description
This is an image classification model based on a WideResNet-2-28, trained using the AdaMatch method by Berthelot et al.
The training was based on the example Semi-supervision and domain adaptation with AdaMatch on URL by Sayak Paul.
The main difference to the training in the URL example is t... | [
"## Model description\n\nThis is an image classification model based on a WideResNet-2-28, trained using the AdaMatch method by Berthelot et al. \n\n The training was based on the example Semi-supervision and domain adaptation with AdaMatch on URL by Sayak Paul. \n\nThe main difference to the training in the URL ex... | [
"TAGS\n#keras #tensorboard #semi-supervised #image classification #domain adaption #dataset-MNIST #dataset-SVHN #arxiv-1605.07146 #arxiv-2106.04732 #has_space #region-us \n",
"## Model description\n\nThis is an image classification model based on a WideResNet-2-28, trained using the AdaMatch method by Berthelot e... |
text-classification | transformers | ---
co2_eq_emissions: 0.021794705501614994
datasets:
- justpyschitry/autotrain-data-Psychiatry_Article_Identifier
language: unk
tags: "autotrain, psychiatry, ICD-11"
widget:
-
text: "I love AutoTrain 🤗"
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 990132820
- CO... | {} | justpyschitry/Medical_Article_Classifier_by_ICD-11_Chapter | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"doi:10.57967/hf/0037",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T20:35:35+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #doi-10.57967/hf/0037 #autotrain_compatible #endpoints_compatible #region-us
| ---
co2_eq_emissions: 0.021794705501614994
datasets:
- justpyschitry/autotrain-data-Psychiatry_Article_Identifier
language: unk
tags: "autotrain, psychiatry, ICD-11"
widget:
-
text: "I love AutoTrain "
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 990132820
- CO2 ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 990132820\n- CO2 Emissions (in grams): 0.021794705501614994",
"## Validation Metrics\n\n- Loss: 0.3959168493747711\n- Accuracy: 0.9141004862236629\n- Macro F1: 0.8984327823035179\n- Micro F1: 0.9141004862236629\n- Weighted... | [
"TAGS\n#transformers #pytorch #bert #text-classification #doi-10.57967/hf/0037 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 990132820\n- CO2 Emissions (in grams): 0.021794705501614994",
"## Validation Metr... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 990132822
- CO2 Emissions (in grams): 13.4308931494349
## Validation Metrics
- Loss: 0.3777158558368683
- Accuracy: 0.9177471636952999
- Macro F1: 0.9082952086962773
- Micro F1: 0.9177471636952999
- Weighted F1: 0.917537643090580... | {"language": "unk", "tags": "autotrain", "datasets": ["justpyschitry/autotrain-data-Psychiatry_Article_Identifier"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 13.4308931494349} | justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:justpyschitry/autotrain-data-Psychiatry_Article_Identifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T20:36:07+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-justpyschitry/autotrain-data-Psychiatry_Article_Identifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 990132822
- CO2 Emissions (in grams): 13.4308931494349
## Validation Metrics
- Loss: 0.3777158558368683
- Accuracy: 0.9177471636952999
- Macro F1: 0.9082952086962773
- Micro F1: 0.9177471636952999
- Weighted F1: 0.917537643090580... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 990132822\n- CO2 Emissions (in grams): 13.4308931494349",
"## Validation Metrics\n\n- Loss: 0.3777158558368683\n- Accuracy: 0.9177471636952999\n- Macro F1: 0.9082952086962773\n- Micro F1: 0.9177471636952999\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-justpyschitry/autotrain-data-Psychiatry_Article_Identifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 990... |
null | null | a blue hegehog | {"license": "wtfpl"} | BigBoyJimmy3256/sonic | null | [
"license:wtfpl",
"region:us"
] | null | 2022-06-15T21:07:59+00:00 | [] | [] | TAGS
#license-wtfpl #region-us
| a blue hegehog | [] | [
"TAGS\n#license-wtfpl #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. -->
# opus-mt-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["news_commentary"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ar-en-finetuned-ar-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "news_commentary",... | Asia-N/opus-mt-ar-en-finetuned-ar-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:news_commentary",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T21:12:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-news_commentary #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ar-en-finetuned-ar-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on the news\_commentary dataset.
It achieves the following results on the evaluation set:
* Loss: 10.6102
* Bleu: 32.5327
* Gen Len: 56.234
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-09\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 #marian #text2text-generation #generated_from_trainer #dataset-news_commentary #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* ... |
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. -->
# mbert2mbert-finetune-fa
This model is a fine-tuned version of [](https://huggingface.co/) on the pn_summary dataset.
## Model d... | {"tags": ["summarization", "fa", "mbert", "mbert2mbert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["pn_summary"], "model-index": [{"name": "mbert2mbert-finetune-fa", "results": []}]} | eslamxm/mbert2mbert-finetune-fa | null | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"mbert",
"mbert2mbert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:pn_summary",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T21:17:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #fa #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #region-us
|
# mbert2mbert-finetune-fa
This model is a fine-tuned version of [](URL on the pn_summary dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
Th... | [
"# mbert2mbert-finetune-fa\n\nThis model is a fine-tuned version of [](URL on the pn_summary 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 #encoder-decoder #text2text-generation #summarization #fa #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-pn_summary #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbert2mbert-finetune-fa\n\nThis model is a fine-tuned versi... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | fourthbrain-demo/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T21:18:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3023
- Accuracy: 0.8767
- F1: 0.8771
## Model description
More information needed
## Intended uses & limitations
More ... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3023\n- Accuracy: 0.8767\n- F1: 0.8771",
"## Model description\n\nMore information needed",
"## Intended uses & ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt... |
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-emotion
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](http... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-emotion", "results": []}]} | Willy/bert-base-spanish-wwm-cased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T21:32:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-spanish-wwm-cased-finetuned-emotion
=============================================
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: 0.5558
* Accuracy: 0.7630
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training... | [
"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: 8\n* eval\\_b... |
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/1474526156430798849/0Z_z... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hotdogsladies/1655334112277/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hotdogsladies | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T22:00:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Merlin Mann
@hotdogsladies
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/1483397012657688577/19JE... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/skysports/1655334298376/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/skysports | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T22:03:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Sky Sports
@skysports
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/1165801400/43f-logo-squa... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/43folders-hotdogsladies/1655334875186/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/43folders-hotdogsladies | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-15T22:10:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
43 Folders & Merlin Mann
@43folders-hotdogsladies
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 repor... | [] | [
"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
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-NLP-IE", "results": []}]} | Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-15T22:25:26+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
============================================
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: 0.6260
* Accuracy: 0.7015
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: 2",
"### 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\\_... |
null | 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", "binary-classification"]} | mindwrapped/pokemon-card-checker | null | [
"fastai",
"resnet",
"computer-vision",
"classification",
"binary-classification",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-06-15T23:42:56+00:00 | [] | [] | TAGS
#fastai #resnet #computer-vision #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 #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\nHere is a colab notebook... |
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/1519208550865653760/gxiN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pronewchaos/1655352793305/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pronewchaos | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T00:03:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Saitoshi Nanomoto ️🟥
@pronewchaos
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 dat... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Test-demo-colab
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Test-demo-colab", "results": []}]} | YYSH/Test-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T01:32:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| Test-demo-colab
===============
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9479
* Wer: 0.6856
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_s... |
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/1527251112604184576/3dKV... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/acai28/1655350773093/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/acai28 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T02:32:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
alec
@acai28
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"
] |
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/1271291765719351297/_NdP... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/fushidahardy/1655350909485/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/fushidahardy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T02:38:36+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Shintaro Fushida-Hardy
@fushidahardy
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"
] |
null | null | Dog eating fish
| {} | Wahoo27/k | null | [
"region:us"
] | null | 2022-06-16T03:07:32+00:00 | [] | [] | TAGS
#region-us
| Dog eating fish
| [] | [
"TAGS\n#region-us \n"
] |
null | transformers | # LayoutLMv3
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objective... | {"language": "zh", "license": "cc-by-nc-sa-4.0"} | microsoft/layoutlmv3-base-chinese | null | [
"transformers",
"pytorch",
"layoutlmv3",
"zh",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T03:38:00+00:00 | [
"2204.08387"
] | [
"zh"
] | TAGS
#transformers #pytorch #layoutlmv3 #zh #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
| LayoutLMv3
==========
Microsoft Document AI | GitHub
Model description
-----------------
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example,... | [] | [
"TAGS\n#transformers #pytorch #layoutlmv3 #zh #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1191610860973764608/vH0n... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/shammytv/1655356038315/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/shammytv | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T03:38:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Swift
@shammytv
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"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for St... | {"library_name": "stable-baselines3", "tags": ["BeamRiderNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BeamRiderNoFrameskip-v4... | Corianas/dqn-BeamRiderNoFrameskip-v4_2 | null | [
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T03:38:44+00:00 | [] | [] | TAGS
#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing BeamRiderNoFrameskip-v4
This is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents include... | [
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained age... | [
"TAGS\n#stable-baselines3 #BeamRiderNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing BeamRiderNoFrameskip-v4\nThis is a trained model of a DQN agent playing BeamRiderNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL ... |
image-classification | transformers |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T04:01:52+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releas... | [
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The tea... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-... |
image-classification | transformers |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-base-patch4-window12to24-192to384-22kto1k-ft | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T04:15:03+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releas... | [
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The tea... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-... |
image-classification | transformers |
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repo... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T04:23:35+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (base-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team releas... | [
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The tea... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (base-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-Test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilber... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-Test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_t... | Seema09/finetuning-sentiment-model-Test | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T04:58:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-Test
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2703
- Accuracy: 0.904
- F1: 0.9048
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# finetuning-sentiment-model-Test\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2703\n- Accuracy: 0.904\n- F1: 0.9048",
"## Model description\n\nMore information needed",
"## Intended uses & limitations... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-Test\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ... |
image-classification | transformers |
# Swin Transformer v2 (large-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this rep... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft | null | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T05:09:46+00:00 | [
"2111.09883"
] | [] | TAGS
#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Swin Transformer v2 (large-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository.
Disclaimer: The team relea... | [
"# Swin Transformer v2 (large-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. \n\nDisclaimer: The te... | [
"TAGS\n#transformers #pytorch #swinv2 #image-classification #vision #dataset-imagenet-1k #arxiv-2111.09883 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Swin Transformer v2 (large-sized model) \n\nSwin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet... |
null | null | Things worth to meantion:
1. The float type teacher embedding is quantized into a sequence of
8-bit integer codebook indexes.
2. a middle layer 36(1-based) out of total 48 layers is used to extract
teacher embeddings.
3. a middle layer 6(1-based) out of total 6 layers is used to extract
student embeddings.
| {} | Zengwei/pruned_transducer_stateless6_hubert_xtralarge_ll60k_finetune_ls960 | null | [
"region:us"
] | null | 2022-06-16T05:16:05+00:00 | [] | [] | TAGS
#region-us
| Things worth to meantion:
1. The float type teacher embedding is quantized into a sequence of
8-bit integer codebook indexes.
2. a middle layer 36(1-based) out of total 48 layers is used to extract
teacher embeddings.
3. a middle layer 6(1-based) out of total 6 layers is used to extract
student embeddings.
| [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **SkiingNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SkiingNoFrameskip-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 B... | {"library_name": "stable-baselines3", "tags": ["SkiingNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SkiingNoFrameskip-v4", "ty... | Corianas/SkiingNoFrameskip-v4_ScoringTest | null | [
"stable-baselines3",
"SkiingNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T05:20:38+00:00 | [] | [] | TAGS
#stable-baselines3 #SkiingNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing SkiingNoFrameskip-v4
This is a trained model of a PPO agent playing SkiingNoFrameskip-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.
##... | [
"# PPO Agent playing SkiingNoFrameskip-v4\nThis is a trained model of a PPO agent playing SkiingNoFrameskip-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 in... | [
"TAGS\n#stable-baselines3 #SkiingNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing SkiingNoFrameskip-v4\nThis is a trained model of a PPO agent playing SkiingNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | Rajesh222/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T05:57:32+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2133
* Accuracy: 0.9265
* F1: 0.9265
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | mindwrapped/bom-generator-awd-lstm | null | [
"fastai",
"has_space",
"region:us"
] | null | 2022-06-16T06:03:31+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #has_space #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (d... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-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 Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | Corianas/PPO-QbertNoFrameskip-v4_1 | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T06:17:21+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-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.
## U... | [
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-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 incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
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 | 74.5 | 74.5 |
| test | 74.9 | 74.8 |
| {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-repnum_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T06:24:49+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: 74.5, F1macro: 74.5
Set: test, F1micro: 74.9, F1macro: 74.8
| [] | [
"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 | 75.6 | 75.3 |
| test | 76.1 | 75.8 |
| {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-repnum_wl-rua_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T06:27:43+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: 75.6, F1macro: 75.3
Set: test, F1micro: 76.1, F1macro: 75.8
| [] | [
"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 | 73.5 | 73.3 |
| test | 73.8 | 73.6 |
| {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-base-finetuned-rua_wl_3_classes | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T06:29:41+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: 73.5, F1macro: 73.3
Set: test, F1micro: 73.8, F1macro: 73.6
| [] | [
"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 | 84.5 | 84.3 |
| test | 85.2 | 85.1 |
| {"language": ["fr"], "tags": ["nli"], "metrics": ["f1"]} | waboucay/camembert-large-finetuned-repnum_wl-rua_wl | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T06:32:42+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: 84.5, F1macro: 84.3
Set: test, F1micro: 85.2, F1macro: 85.1
| [] | [
"TAGS\n#transformers #pytorch #camembert #text-classification #nli #fr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization | transformers |
Task: Summarization
## Usage
```python
from transformers import PegasusForConditionalGeneration,BertTokenizer
class PegasusTokenizer(BertTokenizer):
model_input_names = ["input_ids", "attention_mask"]
def __init__(self, **kwargs):
super().__init__(**kwargs)
# super().__init__(**kwargs)
... | {"language": "zh", "tags": ["summarization"], "inference": true} | dongxq/test_model | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T07:12:02+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #zh #autotrain_compatible #endpoints_compatible #region-us
|
Task: Summarization
## Usage
If you find the resource is useful, please cite the following website in your paper.
| [
"## Usage\n\n\nIf you find the resource is useful, please cite the following website in your paper."
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"## Usage\n\n\nIf you find the resource is useful, please cite the following website in your paper."
] |
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... | QuickSilver007/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-16T07:23:46+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 |
# KB-BERT distilled base model (cased)
This model is a distilled version of [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased). It was distilled using Swedish data, the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The code for the distilla... | {"language": "sv", "license": "apache-2.0"} | Addedk/kbbert-distilled-cased | null | [
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"sv",
"arxiv:2103.06418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T07:33:00+00:00 | [
"2103.06418"
] | [
"sv"
] | TAGS
#transformers #pytorch #tf #bert #fill-mask #sv #arxiv-2103.06418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# KB-BERT distilled base model (cased)
This model is a distilled version of KB-BERT. It was distilled using Swedish data, the 2010-2015 portion of the Swedish Culturomics Gigaword Corpus. The code for the distillation process can be found here. This was done as part of my Master's Thesis: *Task-agnostic knowledge dis... | [
"# KB-BERT distilled base model (cased)\n\nThis model is a distilled version of KB-BERT. It was distilled using Swedish data, the 2010-2015 portion of the Swedish Culturomics Gigaword Corpus. The code for the distillation process can be found here. This was done as part of my Master's Thesis: *Task-agnostic knowled... | [
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #sv #arxiv-2103.06418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# KB-BERT distilled base model (cased)\n\nThis model is a distilled version of KB-BERT. It was distilled using Swedish data, the 2010-2015 portion of the Swedis... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | good-ai-club/NBB | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T07:37:56+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
object-detection | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta... | {"library_name": "keras", "tags": ["object-detection", "some_other_tag"]} | johko/wideresnet28-2-mnist | null | [
"keras",
"tensorboard",
"object-detection",
"some_other_tag",
"has_space",
"region:us"
] | null | 2022-06-16T07:42:38+00:00 | [] | [] | TAGS
#keras #tensorboard #object-detection #some_other_tag #has_space #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #object-detection #some_other_tag #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
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. -->
# sentence_bert-base-uncased-finetuned-SENTENCE
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "sentence_bert-base-uncased-finetuned-SENTENCE", "results": []}]} | ali2066/sentence_bert-base-uncased-finetuned-SENTENCE | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T07:45:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sentence\_bert-base-uncased-finetuned-SENTENCE
==============================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4834
* Precision: 0.8079
* Recall: 1.0
* F1: 0.8938
* Accuracy: 0.8079
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### 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: 0.0001\n* train\\_batch\... |
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/1081285419512127488/Mkb9... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/minusgn | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-16T08:00:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Isak Vik
@minusgn
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"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | Salvatore/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T08:09:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0997
* Proteinmutation F1: 0.1309
* Snp F1: 0.1953
* Dnamutation F1: 0.3778
* Precision: 0.2380
* Recall: 0.2416
* F1: 0.2398
* Accuracy... | [
"### 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 #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Corianas/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-Slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4", "type": "FrozenLake-v1-4x4"}, "met... | Corianas/q-FrozenLake-v1-4x4-Slippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-16T08:14:52+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4 #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 #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 |
<!-- 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-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False
This model is a fine-tuned version of [bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False", "results": []}]} | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T08:25:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased\_token\_itr0\_0.0001\_TRAIN\_all\_TEST\_null\_\_second\_train\_set\_NULL\_False
=================================================================================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the eval... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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. -->
# xlm-roberta-base-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["hi_ner-original"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "xlm-roberta-base-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "hi_ner-orig... | roymukund/xlm-roberta-base-finetuned-ner | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:hi_ner-original",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-16T08:30:15+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-hi_ner-original #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner
==============================
This model is a fine-tuned version of xlm-roberta-base on the hi\_ner-original dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2314
* Precision: 0.7366
* Recall: 0.6771
* F1: 0.7056
* Accuracy: 0.9359
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-hi_ner-original #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... |
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