modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
BME-TMIT/foszt2oszt | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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} | 15 | 2023-01-23T22:28:08Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1744.17 +/- 508.86
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BSC-LT/RoBERTalex | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"transformers",
"legal",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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} | 24 | 2023-01-23T23:15:32Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# lewispons/Email-classifier-v2
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("lewispons/Email-classifier-v2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
BSC-LT/roberta-base-biomedical-clinical-es | [
"pytorch",
"roberta",
"fill-mask",
"es",
"arxiv:2109.03570",
"arxiv:2109.07765",
"transformers",
"biomedical",
"clinical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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}
} | 27 | null | ---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results: []
---
<!-- 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. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BSC-LT/roberta-base-bne-capitel-ner-plus | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
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}
}
} | 9 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### eduardosflopes2 Dreambooth model trained by eduardosflopes with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
BSC-LT/roberta-base-bne | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 594 | null | ---
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: LunarLander-v2
metrics:
- type: mean_reward
value: 239.85 +/- 21.70
name: mean_reward
verified: false
---
# **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 import load_from_hub
...
```
|
BSC-LT/roberta-large-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 15 | null | Scylla model mixed using AnythingV3 and scylla only tagged images from danbooru
Dataset of 233 images with complete tag lists
This model is amazing with highres fix you can get some amazing results way better than I had hoped for extremely happy with the results
More monster girl models on the way feel free to request your favs :)
Scylla: 1girl, scylla, tentacles, full body, masterpiece, best quality,
 |
BSen/wav2vec2-large-xls-r-300m-turkish-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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}
}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
model-index:
- name: Arabic-English-opus100
results: []
---
<!-- 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. -->
# Arabic-English-opus100
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus100 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BW/TEST | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_Human_Activity_Recognition
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8380952380952381
language:
- en
---
# vit-base-patch16-224-in21k_Human_Activity_Recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
It achieves the following results on the evaluation set:
- Loss: 0.7403
- Accuracy: 0.8381
- Weighted f1: 0.8388
- Micro f1: 0.8381
- Macro f1: 0.8394
- Weighted recall: 0.8381
- Micro recall: 0.8381
- Macro recall: 0.8390
- Weighted precision: 0.8421
- Micro precision: 0.8381
- Macro precision: 0.8424
## Model description
This is a multiclass image classification model of humans doing different activities.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/ViT-Human%20Action_Recogniton.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.0814 | 1.0 | 630 | 0.7368 | 0.7794 | 0.7795 | 0.7794 | 0.7798 | 0.7794 | 0.7794 | 0.7797 | 0.7896 | 0.7794 | 0.7896 |
| 0.5149 | 2.0 | 1260 | 0.6439 | 0.8060 | 0.8049 | 0.8060 | 0.8036 | 0.8060 | 0.8060 | 0.8051 | 0.8136 | 0.8060 | 0.8130 |
| 0.3023 | 3.0 | 1890 | 0.7026 | 0.8254 | 0.8272 | 0.8254 | 0.8278 | 0.8254 | 0.8254 | 0.8256 | 0.8335 | 0.8254 | 0.8345 |
| 0.0507 | 4.0 | 2520 | 0.7414 | 0.8317 | 0.8342 | 0.8317 | 0.8348 | 0.8317 | 0.8317 | 0.8321 | 0.8427 | 0.8317 | 0.8438 |
| 0.0128 | 5.0 | 3150 | 0.7403 | 0.8381 | 0.8388 | 0.8381 | 0.8394 | 0.8381 | 0.8381 | 0.8390 | 0.8421 | 0.8381 | 0.8424 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1 |
BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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} | 8 | 2023-02-15T18:17:04Z | ---
thumbnail: https://s3.amazonaws.com/moonup/production/uploads/1676494961340-6305db1fcfbde33ef7d480ff.jpeg
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
- safetensors
---
# Alloy Models
## Brass Mix
```
masterpiece, high quality, 1girl, green hair, pirate, parrot, looking at viewer, ultra detailed,
Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, nostrils, censored, realistic,
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3837415330, Size: 512x768, Model hash: 118c467c0b, Model: alloymix-brass-a-fp16, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
```
- Brass c sample
```
1girl, solo, white hair, dress, light smile, looking at viewer, room, wall, plant, flowers,
Negative prompt: worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, nostrils
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 5, Seed: 2737241409, Size: 896x640, Model hash: 58b6c0666f, Model: alloymix-brass-c-fp16, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent
```
|
BigSalmon/MrLincoln11 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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}
} | 9 | null | ---
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: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 502.00 +/- 209.66
name: mean_reward
verified: false
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga alyssamarieloo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga alyssamarieloo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga alyssamarieloo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 500000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/MrLincoln2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | 2023-01-24T03:52:31Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1308443889695690752/P-Cyxhov_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">penelope scott lyrics</div>
<div style="text-align: center; font-size: 14px;">@pscottbot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from penelope scott lyrics.
| Data | penelope scott lyrics |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 114 |
| Tweets kept | 3136 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yr07xib/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pscottbot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/j8qcpkm8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j8qcpkm8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/pscottbot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BigSalmon/MrLincoln5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null | ---
library_name: diffusers
pipeline_tag: text-to-image
--- |
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 9 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- 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 you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6874 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 6874,
"warmup_steps": 688,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigSalmon/MrLincolnBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
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}
} | 8 | null | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Vin14-P3
results: []
---
<!-- 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. -->
# Vin14-P3
This model is a fine-tuned version of [HuyenNguyen/Vin12-P3](https://huggingface.co/HuyenNguyen/Vin12-P3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3766
- Wer: 24.0523
## 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:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1675 | 0.57 | 100 | 0.3491 | 23.3601 |
| 0.0998 | 1.15 | 200 | 0.3407 | 22.3272 |
| 0.0872 | 1.72 | 300 | 0.3603 | 23.7776 |
| 0.0323 | 2.3 | 400 | 0.3766 | 24.0523 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-tr-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_8_0
type: common_voice_8_0
config: sw
split: test[:400]
args: sw
metrics:
- name: Wer
type: wer
value: 0.97
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tr-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4900
- Wer: 0.97
## 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:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5497 | 0.4 | 50 | 2.9819 | 1.0 |
| 2.8809 | 0.8 | 100 | 2.8873 | 1.0 |
| 2.8416 | 1.2 | 150 | 2.8427 | 1.0 |
| 2.8145 | 1.6 | 200 | 2.8067 | 1.0 |
| 2.747 | 2.0 | 250 | 2.7092 | 1.0 |
| 2.1095 | 2.4 | 300 | 1.3472 | 1.0 |
| 0.9546 | 2.8 | 350 | 0.7708 | 0.9975 |
| 0.6104 | 3.2 | 400 | 0.6317 | 0.9825 |
| 0.4941 | 3.6 | 450 | 0.5427 | 0.97 |
| 0.4345 | 4.0 | 500 | 0.5314 | 0.975 |
| 0.3327 | 4.4 | 550 | 0.4927 | 0.9625 |
| 0.3099 | 4.8 | 600 | 0.4900 | 0.97 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 17 | null | ---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br>
- Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br>
- `split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
- `original` version is only compatible with CPU & GPU option.<br>
# Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
# Shady Art OFFICIAL:
Source(s): [Hugging Face](https://huggingface.co/ShadyART/Shady_Art_Official) - [CivitAI](https://civitai.com/models/4515/shady-art-official)
This is my personal model based on SD 1.5.
I have personally tested it creating more than 10000 unique images and it has always met my expectations. Positive and negative prompts affect the image in exactly the same way, so you'll have to play around with it!
Info:
-Close-up portraits, half-length photos and full-body photos give the best results but you can generate everything.
-The faces and in particular the eyes, if described properly, are surreal in how beautiful they are as this model generates beautiful faces by default, therefore positive prompts like "symmetrical face, perfect face, symmetrical eyes etc..." are useless and sometimes compromise the result, if necessary use negative prompts like "deformed, disfigured etc..." and the face will come out perfect.
-This model has the ability to also create landscape images (fantasy or not) but you will have to waste more time on it, as it is focused on"subjects".
-Be creative, describe the image in great detail, this guarantees a better overall result.
-NSFW have wonderful results both in txt2image and in img2img, if you want to transform a non-NSFW image into NSFW and img2img does not give good results, use Inpaint and you will succeed 100%.
-Euler a, DPM++ 2M a Karras and DDIM seem to give the best results
Special thanks to sovereignrk and logoth for helping me with some new prompts! |
BigSalmon/T52 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"prefix": "translate English to German: "
},
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"num_beams": 4,
"prefix": "translate English to French: "
},
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"early_stopping": true,
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"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Testing_rolls_royce Dreambooth model trained by JacobPerera with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
BigSalmon/T5Salmon2 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"T5ForConditionalGeneration"
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},
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},
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"prefix": "translate English to Romanian: "
}
}
} | 13 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Aman6917/autotrain-data-tm4_2_big
co2_eq_emissions:
emissions: 14.618973710629989
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 3033986980
- CO2 Emissions (in grams): 14.6190
## Validation Metrics
- Loss: 0.000
- Rouge1: 100.000
- Rouge2: 100.000
- RougeL: 100.000
- RougeLsum: 100.000
- Gen Len: 110.456
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Aman6917/autotrain-tm4_2_big-3033986980
``` |
Blabla/Pipipopo | []
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} | 0 | 2023-01-24T07:37:06Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pbm-cartpole-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Blackmist786/DialoGPt-small-transformers4 | [
"pytorch"
]
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} | 4 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Testing_rolls_royce_100_steps Dreambooth model trained by JacobPerera with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
BonjinKim/dst_kor_bert | [
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
]
| null | {
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} | 5 | null | ---
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: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 449.00 +/- 125.50
name: mean_reward
verified: false
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jwright94 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jwright94 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jwright94
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 75000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Boondong/Wandee | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1115.88 +/- 91.49
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Botslity/Bot | []
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} | 0 | null | ---
title: Protengen Web UI
emoji: 🪄🖼️
colorFrom: red
colorTo: pink
sdk: gradio
sdk_version: 3.15.0
app_file: app.py
pinned: true
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
BrianTin/MTBERT | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1596
## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.224 | 1.0 | 5533 | 1.1606 |
| 0.9626 | 2.0 | 11066 | 1.1240 |
| 0.7619 | 3.0 | 16599 | 1.1596 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: tw-sentiment-finetuned
results: []
metrics:
- accuracy
---
<!-- 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. -->
# tw-sentiment-finetuned
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2039
- Train Accuracy: 0.9171
- Validation Loss: 0.4805
- Validation Accuracy: 0.8237
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4620 | 0.7977 | 0.3893 | 0.8332 | 0 |
| 0.3238 | 0.8596 | 0.4674 | 0.8362 | 1 |
| 0.2039 | 0.9171 | 0.4805 | 0.8237 | 2 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Tokenizers 0.13.2 |
CALM/backup | [
"lean_albert",
"transformers"
]
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} | 4 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: kostasang/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
"model_type": "bert",
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} | 37 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: kostasang/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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} | 54 | 2023-01-24T10:31:14Z | ---
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: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ludsil/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"has_space"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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} | 19,850 | 2023-01-24T10:38:46Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.62
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ludsil/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
} | 45 | null | ---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="lewtun/dummy-trl-model")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("lewtun/dummy-trl-model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("lewtun/dummy-trl-model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
}
} | 34 | 2023-01-24T10:43:36Z | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 63 | null | ---
language:
- en
tags:
- anime
---
# Trained LoRAs
- [Overview](#overview)
- [Previews](#previews)
# Overview
I will upload all of LoRAs that I trained here on my free time. The model I mostly used with these previews will be [AnyV4.5](https://huggingface.co/andite/anything-v4.0) or [AOM2](https://huggingface.co/WarriorMama777/OrangeMixs). I haven't tested it on other models but it may or may not work :/ I also just started learning on doing training so the quality may not be good as I'm still a noob with this stuff 😅 (For example, you may still need to specify some character traits on the prompt to be more accurate.)
Some negative embeddings I may use on sample images will be bad-prompt, bad-artist, or bad-image.
# Previews
- [Uma Musume: Pretty Derby](#uma-musume-pretty-derby)
1. [Satono Diamond](#satono-diamond)
2. [Mihono Bourbon](#mihono-bourbon)
- [Style LoRAs](#art-styles)
## Uma Musume: Pretty Derby
- ### Satono Diamond
<img src="https://huggingface.co/OrangeCatapult20/trained-loras/resolve/main/previews/1.png" width="512" height="768">
<details>
<summary>Sample Prompt</summary>
<pre>
masterpiece, best quality, 1girl, solo, satono diamond, horse ears
<b>Negative prompt:</b> lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, low quality, normal quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts))
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3393568171, Size: 512x768, Model hash: 0fc198c490, Clip skip: 2, ENSD: 31337, AddNet Enabled: True, AddNet Module 1: LoRA, AddNet Model 1: satono_diamond(aff3460f), AddNet Weight 1: 0.54
</pre>
</details>
- ### Mihono Bourbon
<img src="https://huggingface.co/OrangeCatapult20/trained-loras/resolve/main/previews/2.png" width="512" height="768">
<details>
<summary>Sample Prompt</summary>
<pre>
masterpiece, best quality, ray tracing, bloom, 1girl, solo, mihono bourbon, horse ears, blue eyes, umamusume, bare shoulders, closed mouth, ahoge, pleated skirt, detached sleeves, necktie, miniskirt, thighhighs, leotard, covered navel, highleg leotard, lowleg skirt
<b>Negative prompt:</b> (bad-image-v2:0.8), lowres, mutated hands and fingers, extra legs, extra limbs, fused fingers, simple background, white background, letterboxed, out of frame, border, monster, mutated, bad anatomy, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality:1.3), normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, copyright name, watermark
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 8, Seed: 3193256216, Size: 512x768, Model hash: 0fc198c490, Clip skip: 2, ENSD: 31337, AddNet Enabled: True, AddNet Module 1: LoRA, AddNet Model 1: mihono_bourbon(e038379e), AddNet Weight 1: 1
</pre>
</details>
# Art Styles
Style LoRAs strength may vary between models used and other LoRAs being used alongside with it, but 0.65 usually works.
- ### Lillly
[Artist Twitter](https://twitter.com/lillly____?s=20)
<img src="https://huggingface.co/OrangeCatapult20/trained-loras/resolve/main/previews/3.png" width="512" height="768">
<details>
<summary>Sample Prompt</summary>
<pre>
masterpiece, best quality, ultra-detailed, bloom, delicate and beautiful, (from above:1.2), 1girl, solo, beautiful eyes, fox ears, fox tail, fox girl, large breasts, red hair, white dress, see-through, breasts out, open clothes, bottomless, water on breasts, forest background <lora:lillly-style:0.65>
<b>Negative prompt:</b> (painting by bad-artist-anime:0.9), (painting by bad-artist:0.9), watermark, text, error, blurry, jpeg artifacts, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, artist name, (worst quality, low quality:1.4), bad anatomy
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 6.5, Seed: 4158263580, Size: 512x768, Model hash: f773383dbc, Clip skip: 2, ENSD: 31337
</pre>
</details>
- ### Kazutake Hazano
[Artist Twitter](https://twitter.com/TEIGI_3?s=20)
<img src="https://huggingface.co/OrangeCatapult20/trained-loras/resolve/main/previews/4.png" width="768" height="512">
<details>
<summary>Sample Prompt</summary>
<pre>
hazano style, masterpiece, best quality, ultra-detailed, illustration, official art, lens flare, (detailed light),(night time),((an extremely delicate and beautiful)),((hull body)),(dynamic angle),((beautiful detailed eyes)),(beautiful moon light),((gothic lolita)),(clothes flutter),(black hair:1.5),(blue eyes),((swimming under water)),(beautiful water),(bubbles in water),(shimmering water),((sea fireflie)),(underwater flowers back ground) <lora:hazano-style:0.65>
<b>Negative prompt:</b> (bad-image-v2:0.8), lowres, mutated hands and fingers, extra legs, extra limbs, fused fingers, simple background, white background, letterboxed, out of frame, border, monster, mutated, bad anatomy, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality:1.3), normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, copyright name, watermark
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Seed: 660771506, Size: 768x512, Model hash: e4b17ce185, Model: anything-v4.5-pruned, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 1.5, Hires upscaler: Lanczos
</pre>
</details> |
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
],
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}
} | 62 | 2023-01-24T14:10:22Z | ---
license: apache-2.0
tags:
- vision
- depth-estimation
- generated_from_trainer
model-index:
- name: glpn-nyu-finetuned-diode-230124-104649
results: []
---
<!-- 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. -->
# glpn-nyu-finetuned-diode-230124-104649
This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4340
- Mae: 0.4201
- Rmse: 0.6110
- Abs Rel: 0.4400
- Log Mae: 0.1698
- Log Rmse: 0.2229
- Delta1: 0.3745
- Delta2: 0.6423
- Delta3: 0.8241
## 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:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 48
- seed: 2022
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
| 1.0761 | 1.0 | 72 | 0.5035 | 0.4784 | 0.6697 | 0.5506 | 0.2007 | 0.2592 | 0.3019 | 0.5331 | 0.7997 |
| 0.4776 | 2.0 | 144 | 0.4640 | 0.4494 | 0.6305 | 0.4846 | 0.1853 | 0.2370 | 0.3321 | 0.5850 | 0.7752 |
| 0.4667 | 3.0 | 216 | 0.4852 | 0.4716 | 0.6377 | 0.5477 | 0.1965 | 0.2473 | 0.3105 | 0.5246 | 0.7218 |
| 0.4387 | 4.0 | 288 | 0.4587 | 0.4378 | 0.6223 | 0.4874 | 0.1790 | 0.2343 | 0.3577 | 0.6064 | 0.7906 |
| 0.4612 | 5.0 | 360 | 0.4843 | 0.4610 | 0.6341 | 0.5444 | 0.1906 | 0.2458 | 0.3269 | 0.5602 | 0.7445 |
| 0.4564 | 6.0 | 432 | 0.4605 | 0.4330 | 0.6197 | 0.4901 | 0.1764 | 0.2339 | 0.3775 | 0.6049 | 0.8022 |
| 0.4166 | 7.0 | 504 | 0.4576 | 0.4421 | 0.6256 | 0.4625 | 0.1809 | 0.2322 | 0.3613 | 0.5882 | 0.7685 |
| 0.3922 | 8.0 | 576 | 0.4805 | 0.4537 | 0.6296 | 0.5422 | 0.1875 | 0.2439 | 0.3381 | 0.5612 | 0.7874 |
| 0.3944 | 9.0 | 648 | 0.4601 | 0.4430 | 0.6254 | 0.4762 | 0.1812 | 0.2332 | 0.3545 | 0.5877 | 0.7662 |
| 0.3748 | 10.0 | 720 | 0.4606 | 0.4377 | 0.6221 | 0.4960 | 0.1796 | 0.2354 | 0.3573 | 0.5960 | 0.8031 |
| 0.3749 | 11.0 | 792 | 0.4513 | 0.4377 | 0.6300 | 0.4403 | 0.1786 | 0.2311 | 0.3621 | 0.6083 | 0.7901 |
| 0.4259 | 12.0 | 864 | 0.4834 | 0.4519 | 0.6328 | 0.5462 | 0.1862 | 0.2457 | 0.3521 | 0.5777 | 0.7614 |
| 0.4337 | 13.0 | 936 | 0.4338 | 0.4153 | 0.6214 | 0.4096 | 0.1664 | 0.2248 | 0.4137 | 0.6651 | 0.8037 |
| 0.4032 | 14.0 | 1008 | 0.4640 | 0.4544 | 0.6279 | 0.4922 | 0.1868 | 0.2351 | 0.3286 | 0.5574 | 0.7557 |
| 0.4286 | 15.0 | 1080 | 0.4835 | 0.4651 | 0.6338 | 0.5567 | 0.1929 | 0.2465 | 0.3197 | 0.5449 | 0.7304 |
| 0.397 | 16.0 | 1152 | 0.4713 | 0.4547 | 0.6279 | 0.5121 | 0.1872 | 0.2383 | 0.3312 | 0.5644 | 0.7456 |
| 0.3713 | 17.0 | 1224 | 0.4664 | 0.4375 | 0.6290 | 0.4766 | 0.1780 | 0.2361 | 0.3821 | 0.6036 | 0.7668 |
| 0.4155 | 18.0 | 1296 | 0.4881 | 0.4722 | 0.6367 | 0.5705 | 0.1967 | 0.2494 | 0.3061 | 0.5293 | 0.7220 |
| 0.3822 | 19.0 | 1368 | 0.4819 | 0.4592 | 0.6322 | 0.5526 | 0.1898 | 0.2456 | 0.3357 | 0.5531 | 0.7365 |
| 0.408 | 20.0 | 1440 | 0.4367 | 0.4201 | 0.6141 | 0.4322 | 0.1691 | 0.2232 | 0.3902 | 0.6418 | 0.8083 |
| 0.3698 | 21.0 | 1512 | 0.4461 | 0.4263 | 0.6171 | 0.4454 | 0.1724 | 0.2263 | 0.3850 | 0.6232 | 0.7980 |
| 0.3628 | 22.0 | 1584 | 0.4461 | 0.4254 | 0.6226 | 0.4520 | 0.1724 | 0.2307 | 0.3953 | 0.6324 | 0.7926 |
| 0.3827 | 23.0 | 1656 | 0.4753 | 0.4529 | 0.6364 | 0.5288 | 0.1867 | 0.2450 | 0.3415 | 0.5893 | 0.7678 |
| 0.4378 | 24.0 | 1728 | 0.4779 | 0.4608 | 0.6308 | 0.5422 | 0.1907 | 0.2433 | 0.3247 | 0.5435 | 0.7434 |
| 0.3766 | 25.0 | 1800 | 0.4533 | 0.4415 | 0.6231 | 0.4750 | 0.1802 | 0.2309 | 0.3544 | 0.5796 | 0.7917 |
| 0.3642 | 26.0 | 1872 | 0.4520 | 0.4276 | 0.6224 | 0.4686 | 0.1736 | 0.2322 | 0.3901 | 0.6242 | 0.8048 |
| 0.3503 | 27.0 | 1944 | 0.4451 | 0.4262 | 0.6163 | 0.4574 | 0.1730 | 0.2278 | 0.3721 | 0.6306 | 0.8116 |
| 0.3723 | 28.0 | 2016 | 0.4617 | 0.4451 | 0.6239 | 0.4936 | 0.1824 | 0.2346 | 0.3436 | 0.5864 | 0.7740 |
| 0.3739 | 29.0 | 2088 | 0.4468 | 0.4295 | 0.6209 | 0.4513 | 0.1741 | 0.2285 | 0.3738 | 0.6288 | 0.7954 |
| 0.3699 | 30.0 | 2160 | 0.4494 | 0.4334 | 0.6233 | 0.4682 | 0.1766 | 0.2320 | 0.3684 | 0.6155 | 0.7947 |
| 0.3573 | 31.0 | 2232 | 0.4603 | 0.4385 | 0.6215 | 0.4963 | 0.1793 | 0.2345 | 0.3620 | 0.5948 | 0.7839 |
| 0.3684 | 32.0 | 2304 | 0.4488 | 0.4278 | 0.6195 | 0.4571 | 0.1735 | 0.2290 | 0.3895 | 0.6201 | 0.7970 |
| 0.3911 | 33.0 | 2376 | 0.4499 | 0.4309 | 0.6201 | 0.4636 | 0.1751 | 0.2301 | 0.3839 | 0.6118 | 0.7803 |
| 0.3416 | 34.0 | 2448 | 0.4515 | 0.4298 | 0.6185 | 0.4734 | 0.1748 | 0.2311 | 0.3824 | 0.6152 | 0.7916 |
| 0.3345 | 35.0 | 2520 | 0.4434 | 0.4247 | 0.6163 | 0.4548 | 0.1720 | 0.2274 | 0.3881 | 0.6233 | 0.8077 |
| 0.3436 | 36.0 | 2592 | 0.4561 | 0.4370 | 0.6208 | 0.4926 | 0.1785 | 0.2337 | 0.3586 | 0.5960 | 0.7979 |
| 0.3411 | 37.0 | 2664 | 0.4805 | 0.4629 | 0.6337 | 0.5600 | 0.1920 | 0.2468 | 0.3187 | 0.5448 | 0.7601 |
| 0.3755 | 38.0 | 2736 | 0.4566 | 0.4365 | 0.6235 | 0.4780 | 0.1784 | 0.2335 | 0.3662 | 0.5972 | 0.7941 |
| 0.3456 | 39.0 | 2808 | 0.4665 | 0.4500 | 0.6259 | 0.5163 | 0.1851 | 0.2386 | 0.3368 | 0.5756 | 0.7686 |
| 0.3829 | 40.0 | 2880 | 0.4720 | 0.4527 | 0.6279 | 0.5323 | 0.1871 | 0.2423 | 0.3384 | 0.5656 | 0.7635 |
| 0.3645 | 41.0 | 2952 | 0.4380 | 0.4211 | 0.6133 | 0.4377 | 0.1701 | 0.2234 | 0.3945 | 0.6275 | 0.8056 |
| 0.3654 | 42.0 | 3024 | 0.4228 | 0.4087 | 0.6240 | 0.3844 | 0.1624 | 0.2220 | 0.4339 | 0.6953 | 0.8065 |
| 0.3694 | 43.0 | 3096 | 0.4390 | 0.4183 | 0.6153 | 0.4374 | 0.1683 | 0.2250 | 0.3991 | 0.6509 | 0.8065 |
| 0.329 | 44.0 | 3168 | 0.4559 | 0.4349 | 0.6191 | 0.4912 | 0.1775 | 0.2330 | 0.3611 | 0.6075 | 0.7981 |
| 0.3509 | 45.0 | 3240 | 0.4566 | 0.4341 | 0.6202 | 0.4973 | 0.1774 | 0.2347 | 0.3653 | 0.6168 | 0.7942 |
| 0.3666 | 46.0 | 3312 | 0.4665 | 0.4452 | 0.6239 | 0.5179 | 0.1830 | 0.2379 | 0.3384 | 0.5860 | 0.7844 |
| 0.3948 | 47.0 | 3384 | 0.4570 | 0.4406 | 0.6221 | 0.4883 | 0.1805 | 0.2333 | 0.3504 | 0.5887 | 0.7961 |
| 0.3349 | 48.0 | 3456 | 0.4539 | 0.4372 | 0.6186 | 0.4851 | 0.1789 | 0.2316 | 0.3467 | 0.5966 | 0.8092 |
| 0.3689 | 49.0 | 3528 | 0.4416 | 0.4182 | 0.6136 | 0.4565 | 0.1685 | 0.2270 | 0.3991 | 0.6475 | 0.8157 |
| 0.3477 | 50.0 | 3600 | 0.4417 | 0.4241 | 0.6184 | 0.4513 | 0.1713 | 0.2272 | 0.3802 | 0.6461 | 0.8114 |
| 0.3476 | 51.0 | 3672 | 0.4502 | 0.4333 | 0.6189 | 0.4766 | 0.1763 | 0.2304 | 0.3594 | 0.6120 | 0.8096 |
| 0.3318 | 52.0 | 3744 | 0.4480 | 0.4268 | 0.6167 | 0.4666 | 0.1728 | 0.2287 | 0.3744 | 0.6318 | 0.8080 |
| 0.336 | 53.0 | 3816 | 0.4504 | 0.4266 | 0.6159 | 0.4792 | 0.1730 | 0.2306 | 0.3782 | 0.6248 | 0.8089 |
| 0.3283 | 54.0 | 3888 | 0.4490 | 0.4265 | 0.6184 | 0.4689 | 0.1732 | 0.2305 | 0.3872 | 0.6295 | 0.8037 |
| 0.3465 | 55.0 | 3960 | 0.4371 | 0.4216 | 0.6189 | 0.4399 | 0.1701 | 0.2263 | 0.3866 | 0.6515 | 0.8168 |
| 0.3299 | 56.0 | 4032 | 0.4544 | 0.4377 | 0.6199 | 0.4828 | 0.1787 | 0.2319 | 0.3532 | 0.6004 | 0.7961 |
| 0.3301 | 57.0 | 4104 | 0.4351 | 0.4208 | 0.6151 | 0.4317 | 0.1700 | 0.2234 | 0.3837 | 0.6386 | 0.8147 |
| 0.3314 | 58.0 | 4176 | 0.4347 | 0.4189 | 0.6130 | 0.4373 | 0.1689 | 0.2234 | 0.3889 | 0.6468 | 0.8153 |
| 0.328 | 59.0 | 4248 | 0.4536 | 0.4342 | 0.6187 | 0.4887 | 0.1773 | 0.2326 | 0.3554 | 0.6080 | 0.8052 |
| 0.3153 | 60.0 | 4320 | 0.4393 | 0.4206 | 0.6130 | 0.4515 | 0.1699 | 0.2259 | 0.3854 | 0.6416 | 0.8156 |
| 0.3274 | 61.0 | 4392 | 0.4482 | 0.4275 | 0.6148 | 0.4738 | 0.1740 | 0.2295 | 0.3703 | 0.6176 | 0.8177 |
| 0.3123 | 62.0 | 4464 | 0.4380 | 0.4172 | 0.6139 | 0.4461 | 0.1678 | 0.2259 | 0.4007 | 0.6569 | 0.8189 |
| 0.3269 | 63.0 | 4536 | 0.4395 | 0.4186 | 0.6123 | 0.4574 | 0.1690 | 0.2267 | 0.3881 | 0.6507 | 0.8179 |
| 0.3214 | 64.0 | 4608 | 0.4400 | 0.4229 | 0.6128 | 0.4580 | 0.1714 | 0.2264 | 0.3709 | 0.6391 | 0.8222 |
| 0.3139 | 65.0 | 4680 | 0.4506 | 0.4295 | 0.6169 | 0.4828 | 0.1748 | 0.2315 | 0.3662 | 0.6223 | 0.8150 |
| 0.306 | 66.0 | 4752 | 0.4391 | 0.4210 | 0.6134 | 0.4565 | 0.1702 | 0.2266 | 0.3802 | 0.6481 | 0.8169 |
| 0.3375 | 67.0 | 4824 | 0.4511 | 0.4304 | 0.6177 | 0.4807 | 0.1751 | 0.2314 | 0.3683 | 0.6189 | 0.8063 |
| 0.3199 | 68.0 | 4896 | 0.4409 | 0.4230 | 0.6157 | 0.4615 | 0.1716 | 0.2284 | 0.3796 | 0.6425 | 0.8184 |
| 0.3286 | 69.0 | 4968 | 0.4424 | 0.4242 | 0.6141 | 0.4608 | 0.1721 | 0.2274 | 0.3752 | 0.6317 | 0.8149 |
| 0.3168 | 70.0 | 5040 | 0.4250 | 0.4130 | 0.6118 | 0.4139 | 0.1653 | 0.2191 | 0.3987 | 0.6650 | 0.8192 |
| 0.3316 | 71.0 | 5112 | 0.4391 | 0.4222 | 0.6146 | 0.4486 | 0.1707 | 0.2254 | 0.3795 | 0.6376 | 0.8176 |
| 0.3305 | 72.0 | 5184 | 0.4455 | 0.4273 | 0.6157 | 0.4623 | 0.1738 | 0.2280 | 0.3743 | 0.6173 | 0.8119 |
| 0.3135 | 73.0 | 5256 | 0.4407 | 0.4254 | 0.6159 | 0.4513 | 0.1726 | 0.2264 | 0.3688 | 0.6342 | 0.8149 |
| 0.3364 | 74.0 | 5328 | 0.4421 | 0.4268 | 0.6152 | 0.4561 | 0.1730 | 0.2266 | 0.3675 | 0.6234 | 0.8135 |
| 0.3188 | 75.0 | 5400 | 0.4480 | 0.4317 | 0.6162 | 0.4746 | 0.1760 | 0.2296 | 0.3555 | 0.6132 | 0.8125 |
| 0.3125 | 76.0 | 5472 | 0.4346 | 0.4197 | 0.6120 | 0.4389 | 0.1693 | 0.2230 | 0.3802 | 0.6449 | 0.8225 |
| 0.3179 | 77.0 | 5544 | 0.4437 | 0.4274 | 0.6153 | 0.4633 | 0.1737 | 0.2279 | 0.3686 | 0.6240 | 0.8158 |
| 0.317 | 78.0 | 5616 | 0.4364 | 0.4207 | 0.6127 | 0.4491 | 0.1699 | 0.2250 | 0.3743 | 0.6491 | 0.8237 |
| 0.3303 | 79.0 | 5688 | 0.4464 | 0.4286 | 0.6172 | 0.4742 | 0.1746 | 0.2304 | 0.3679 | 0.6182 | 0.8206 |
| 0.3267 | 80.0 | 5760 | 0.4295 | 0.4147 | 0.6099 | 0.4243 | 0.1666 | 0.2201 | 0.3934 | 0.6519 | 0.8233 |
| 0.3219 | 81.0 | 5832 | 0.4306 | 0.4144 | 0.6101 | 0.4278 | 0.1666 | 0.2209 | 0.3897 | 0.6589 | 0.8240 |
| 0.3271 | 82.0 | 5904 | 0.4378 | 0.4215 | 0.6125 | 0.4465 | 0.1704 | 0.2246 | 0.3787 | 0.6395 | 0.8198 |
| 0.2986 | 83.0 | 5976 | 0.4401 | 0.4253 | 0.6136 | 0.4511 | 0.1724 | 0.2254 | 0.3697 | 0.6270 | 0.8186 |
| 0.3153 | 84.0 | 6048 | 0.4355 | 0.4199 | 0.6111 | 0.4418 | 0.1698 | 0.2232 | 0.3781 | 0.6388 | 0.8250 |
| 0.323 | 85.0 | 6120 | 0.4420 | 0.4262 | 0.6135 | 0.4556 | 0.1731 | 0.2260 | 0.3640 | 0.6235 | 0.8207 |
| 0.308 | 86.0 | 6192 | 0.4359 | 0.4206 | 0.6123 | 0.4421 | 0.1701 | 0.2238 | 0.3774 | 0.6409 | 0.8232 |
| 0.3076 | 87.0 | 6264 | 0.4329 | 0.4185 | 0.6105 | 0.4347 | 0.1688 | 0.2219 | 0.3791 | 0.6471 | 0.8242 |
| 0.3089 | 88.0 | 6336 | 0.4256 | 0.4117 | 0.6083 | 0.4180 | 0.1651 | 0.2189 | 0.3949 | 0.6666 | 0.8253 |
| 0.299 | 89.0 | 6408 | 0.4449 | 0.4300 | 0.6152 | 0.4602 | 0.1749 | 0.2270 | 0.3596 | 0.6151 | 0.8156 |
| 0.3211 | 90.0 | 6480 | 0.4330 | 0.4191 | 0.6106 | 0.4339 | 0.1692 | 0.2218 | 0.3785 | 0.6422 | 0.8252 |
| 0.323 | 91.0 | 6552 | 0.4310 | 0.4167 | 0.6098 | 0.4301 | 0.1680 | 0.2211 | 0.3826 | 0.6508 | 0.8260 |
| 0.3108 | 92.0 | 6624 | 0.4402 | 0.4259 | 0.6130 | 0.4519 | 0.1730 | 0.2251 | 0.3662 | 0.6260 | 0.8188 |
| 0.3201 | 93.0 | 6696 | 0.4300 | 0.4166 | 0.6097 | 0.4312 | 0.1679 | 0.2211 | 0.3834 | 0.6512 | 0.8245 |
| 0.3072 | 94.0 | 6768 | 0.4344 | 0.4217 | 0.6117 | 0.4400 | 0.1706 | 0.2228 | 0.3726 | 0.6356 | 0.8239 |
| 0.3079 | 95.0 | 6840 | 0.4369 | 0.4236 | 0.6121 | 0.4454 | 0.1716 | 0.2238 | 0.3678 | 0.6308 | 0.8241 |
| 0.3192 | 96.0 | 6912 | 0.4328 | 0.4189 | 0.6105 | 0.4362 | 0.1691 | 0.2220 | 0.3774 | 0.6441 | 0.8245 |
| 0.2959 | 97.0 | 6984 | 0.4340 | 0.4203 | 0.6110 | 0.4399 | 0.1700 | 0.2228 | 0.3741 | 0.6409 | 0.8245 |
| 0.3061 | 98.0 | 7056 | 0.4352 | 0.4208 | 0.6112 | 0.4427 | 0.1703 | 0.2234 | 0.3728 | 0.6402 | 0.8249 |
| 0.3294 | 99.0 | 7128 | 0.4329 | 0.4191 | 0.6107 | 0.4372 | 0.1693 | 0.2223 | 0.3762 | 0.6451 | 0.8238 |
| 0.3087 | 100.0 | 7200 | 0.4340 | 0.4201 | 0.6110 | 0.4400 | 0.1698 | 0.2229 | 0.3745 | 0.6423 | 0.8241 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
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}
} | 132 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- 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 you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 125 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1250,
"warmup_steps": 125,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
],
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},
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},
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},
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}
} | 1,862 | 2023-01-24T10:49:00Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/pcuenq/pokemon-lora
These are LoRA adaption weights trained on base model https://huggingface.co/runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset.
## How to Use
The script below loads the base model, then applies the LoRA weights and performs inference:
```Python
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from huggingface_hub import model_info
# LoRA weights ~3 MB
model_path = "pcuenq/pokemon-lora"
info = model_info(model_path)
model_base = info.cardData["base_model"]
pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.unet.load_attn_procs(model_path)
pipe.to("cuda")
image = pipe("Green pokemon with menacing face", num_inference_steps=25).images[0]
image.save("green_pokemon.png")
```
Please, check [our blog post](https://huggingface.co/blog/lora) or [documentation](https://huggingface.co/docs/diffusers/v0.15.0/en/training/lora#text-to-image-inference) for more details.
## Example Images
|
CAMeL-Lab/bert-base-arabic-camelbert-mix | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"Arabic",
"Dialect",
"Egyptian",
"Gulf",
"Levantine",
"Classical Arabic",
"MSA",
"Modern Standard Arabic",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
} | 20,880 | 2023-01-24T10:57:19Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 75 | 2023-01-24T10:57:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-et-children
results: []
language:
- et
library_name: 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. -->
# whisper-large-v2-et-children
This model is a fine-tuned version of [agnesluhtaru/whisper-large-et-ERR2020-v2](https://huggingface.co/agnesluhtaru/whisper-large-et-ERR2020-v2) on an Estonian children's speech dataset.
More information about the model's performance and the data used for evaluation and training:
Luhtaru, Agnes; Jaaska, Rauno; Kruusamäe, Karl; Fishel, Mark (2023). Automatic Transcription for Estonian Children’s Speech. In: Proceedings of the 24th Nordic Conference on Computational Linguistics. [https://openreview.net/forum?id=xbPTfBIUby](https://openreview.net/forum?id=xbPTfBIUby)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0302 | 4.03 | 500 | 0.2971 | 16.2892 |
| 0.0042 | 8.06 | 1000 | 0.3406 | 15.8551 |
| 0.0017 | 12.1 | 1500 | 0.3714 | 15.5585 |
| 0.0009 | 16.13 | 2000 | 0.3934 | 15.6445 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+rocm5.1.1
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2 |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 71 | 2023-01-24T10:58:13Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: newwater/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 21 | 2023-01-24T11:01:03Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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. -->
# vit_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0125
- Accuracy: 1.0
## 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:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1336 | 3.85 | 500 | 0.0125 | 1.0 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 52 | 2023-01-26T20:13:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-libri-train360-colab
results: []
---
<!-- 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-libri-train360-colab
This model is a fine-tuned version of [GW12/wav2vec2-libri-train100-colab](https://huggingface.co/GW12/wav2vec2-libri-train100-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1101
- Wer: 0.1002
## 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:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 3.1196 | 0.02 | 500 | 0.2020 | 0.1494 |
| 0.1695 | 0.04 | 1000 | 0.1600 | 0.1462 |
| 0.1726 | 0.06 | 1500 | 0.1996 | 0.1457 |
| 0.1654 | 0.08 | 2000 | 0.1531 | 0.1448 |
| 0.1665 | 0.1 | 2500 | 0.1582 | 0.1491 |
| 0.1555 | 0.12 | 3000 | 0.1566 | 0.1478 |
| 0.1562 | 0.13 | 3500 | 0.1555 | 0.1501 |
| 0.1604 | 0.15 | 4000 | 0.1465 | 0.1422 |
| 0.1522 | 0.17 | 4500 | 0.1423 | 0.1452 |
| 0.1534 | 0.19 | 5000 | 0.1375 | 0.1431 |
| 0.1576 | 0.21 | 5500 | 0.1872 | 0.1421 |
| 0.1543 | 0.23 | 6000 | 0.1547 | 0.1381 |
| 0.1501 | 0.25 | 6500 | 0.1446 | 0.1381 |
| 0.1508 | 0.27 | 7000 | 0.2108 | 0.1507 |
| 0.1479 | 0.29 | 7500 | 0.1495 | 0.1364 |
| 0.1474 | 0.31 | 8000 | 0.1571 | 0.1406 |
| 0.1475 | 0.33 | 8500 | 0.1570 | 0.1390 |
| 0.1453 | 0.35 | 9000 | 0.1547 | 0.1377 |
| 0.1465 | 0.37 | 9500 | 0.1633 | 0.1336 |
| 0.1424 | 0.38 | 10000 | 0.1344 | 0.1358 |
| 0.1417 | 0.4 | 10500 | 0.2518 | 0.1515 |
| 0.1427 | 0.42 | 11000 | 0.1697 | 0.1409 |
| 0.1434 | 0.44 | 11500 | 0.1649 | 0.1373 |
| 0.1384 | 0.46 | 12000 | 0.1743 | 0.1403 |
| 0.1394 | 0.48 | 12500 | 0.1485 | 0.1407 |
| 0.1392 | 0.5 | 13000 | 0.1421 | 0.1352 |
| 2.3614 | 0.52 | 13500 | 0.9494 | 0.1673 |
| 0.1621 | 0.54 | 14000 | 0.4273 | 0.1539 |
| 0.1454 | 0.56 | 14500 | 0.1764 | 0.1399 |
| 0.1453 | 0.58 | 15000 | 0.1750 | 0.1414 |
| 0.1375 | 0.6 | 15500 | 0.1845 | 0.1410 |
| 0.1436 | 0.62 | 16000 | 0.1583 | 0.1413 |
| 0.1405 | 0.63 | 16500 | 0.1893 | 0.1413 |
| 0.139 | 0.65 | 17000 | 0.2281 | 0.1619 |
| 0.1374 | 0.67 | 17500 | 0.1863 | 0.1413 |
| 0.1386 | 0.69 | 18000 | 0.2301 | 0.1479 |
| 0.1435 | 0.71 | 18500 | 0.2349 | 0.1579 |
| 0.1293 | 0.73 | 19000 | 0.1878 | 0.1461 |
| 0.1311 | 0.75 | 19500 | 0.2092 | 0.1342 |
| 0.1357 | 0.77 | 20000 | 0.1788 | 0.1421 |
| 0.1258 | 0.79 | 20500 | 0.1336 | 0.1302 |
| 0.1284 | 0.81 | 21000 | 0.1459 | 0.1306 |
| 0.1452 | 0.83 | 21500 | 0.1316 | 0.1319 |
| 0.1241 | 0.85 | 22000 | 0.1497 | 0.1285 |
| 0.1292 | 0.87 | 22500 | 0.1417 | 0.1318 |
| 0.1255 | 0.88 | 23000 | 0.1262 | 0.1305 |
| 0.1239 | 0.9 | 23500 | 0.1417 | 0.1302 |
| 0.1237 | 0.92 | 24000 | 0.1704 | 0.1309 |
| 0.1231 | 0.94 | 24500 | 0.1466 | 0.1308 |
| 0.1303 | 0.96 | 25000 | 0.2085 | 0.1392 |
| 0.1252 | 0.98 | 25500 | 0.1514 | 0.1441 |
| 0.1244 | 1.0 | 26000 | 0.1353 | 0.1282 |
| 0.1034 | 1.02 | 26500 | 0.1306 | 0.1279 |
| 0.1035 | 1.04 | 27000 | 0.1785 | 0.1288 |
| 0.1063 | 1.06 | 27500 | 0.1742 | 0.1311 |
| 0.1065 | 1.08 | 28000 | 0.1505 | 0.1269 |
| 0.1093 | 1.1 | 28500 | 0.1394 | 0.1264 |
| 0.1115 | 1.12 | 29000 | 0.1490 | 0.1325 |
| 0.1044 | 1.13 | 29500 | 0.5477 | 0.1736 |
| 0.1003 | 1.15 | 30000 | 0.2347 | 0.1351 |
| 0.1049 | 1.17 | 30500 | 0.2001 | 0.1347 |
| 0.1068 | 1.19 | 31000 | 0.1528 | 0.1255 |
| 0.1069 | 1.21 | 31500 | 0.1528 | 0.1266 |
| 0.1042 | 1.23 | 32000 | 0.2272 | 0.1318 |
| 0.1073 | 1.25 | 32500 | 0.5753 | 0.1869 |
| 0.1021 | 1.27 | 33000 | 0.3459 | 0.1477 |
| 0.1023 | 1.29 | 33500 | 0.2412 | 0.1362 |
| 0.0988 | 1.31 | 34000 | 0.2124 | 0.1319 |
| 0.1047 | 1.33 | 34500 | 0.3733 | 0.1497 |
| 0.1078 | 1.35 | 35000 | 0.1553 | 0.1281 |
| 0.0988 | 1.37 | 35500 | 0.1364 | 0.1239 |
| 0.0957 | 1.38 | 36000 | 0.1484 | 0.1278 |
| 0.1038 | 1.4 | 36500 | 0.1723 | 0.1253 |
| 0.1001 | 1.42 | 37000 | 0.3668 | 0.1648 |
| 0.101 | 1.44 | 37500 | 0.2136 | 0.1339 |
| 0.1022 | 1.46 | 38000 | 0.1140 | 0.1162 |
| 0.0989 | 1.48 | 38500 | 0.1628 | 0.1265 |
| 0.0982 | 1.5 | 39000 | 0.2204 | 0.1376 |
| 0.1012 | 1.52 | 39500 | 0.1716 | 0.1297 |
| 0.1067 | 1.54 | 40000 | 0.1362 | 0.1234 |
| 0.1022 | 1.56 | 40500 | 0.1170 | 0.1178 |
| 0.1011 | 1.58 | 41000 | 0.1578 | 0.1240 |
| 0.0845 | 1.6 | 41500 | 0.1659 | 0.1243 |
| 0.0929 | 1.62 | 42000 | 0.1813 | 0.1310 |
| 0.0904 | 1.63 | 42500 | 0.1309 | 0.1215 |
| 0.0885 | 1.65 | 43000 | 0.1964 | 0.1359 |
| 0.0895 | 1.67 | 43500 | 0.1309 | 0.1179 |
| 0.0855 | 1.69 | 44000 | 0.1472 | 0.1258 |
| 0.0876 | 1.71 | 44500 | 0.1189 | 0.1190 |
| 0.0925 | 1.73 | 45000 | 0.1477 | 0.1209 |
| 0.0866 | 1.75 | 45500 | 0.2537 | 0.1428 |
| 0.0938 | 1.77 | 46000 | 0.1406 | 0.1240 |
| 0.0901 | 1.79 | 46500 | 0.1416 | 0.1201 |
| 0.0839 | 1.81 | 47000 | 0.1323 | 0.1201 |
| 0.0866 | 1.83 | 47500 | 0.1176 | 0.1149 |
| 0.0876 | 1.85 | 48000 | 0.1141 | 0.1139 |
| 0.0857 | 1.87 | 48500 | 0.2148 | 0.1297 |
| 0.089 | 1.88 | 49000 | 0.1707 | 0.1231 |
| 0.0861 | 1.9 | 49500 | 0.1457 | 0.1183 |
| 0.0855 | 1.92 | 50000 | 0.4576 | 0.1654 |
| 0.0808 | 1.94 | 50500 | 0.2264 | 0.1285 |
| 0.0859 | 1.96 | 51000 | 0.1630 | 0.1201 |
| 0.0859 | 1.98 | 51500 | 0.1613 | 0.1165 |
| 0.086 | 2.0 | 52000 | 0.1529 | 0.1196 |
| 0.0769 | 2.02 | 52500 | 0.1258 | 0.1139 |
| 0.0783 | 2.04 | 53000 | 0.1105 | 0.1136 |
| 0.0775 | 2.06 | 53500 | 0.1177 | 0.1128 |
| 0.08 | 2.08 | 54000 | 0.1328 | 0.1156 |
| 0.0765 | 2.1 | 54500 | 0.1229 | 0.1137 |
| 0.0791 | 2.12 | 55000 | 0.1218 | 0.1121 |
| 0.0831 | 2.13 | 55500 | 0.1106 | 0.1135 |
| 0.0769 | 2.15 | 56000 | 0.1466 | 0.1166 |
| 0.0761 | 2.17 | 56500 | 0.1177 | 0.1126 |
| 0.0779 | 2.19 | 57000 | 0.1249 | 0.1120 |
| 0.0749 | 2.21 | 57500 | 0.1258 | 0.1130 |
| 0.0746 | 2.23 | 58000 | 0.1268 | 0.1122 |
| 0.074 | 2.25 | 58500 | 0.1141 | 0.1153 |
| 0.0726 | 2.27 | 59000 | 0.1231 | 0.1107 |
| 0.0771 | 2.29 | 59500 | 0.1393 | 0.1125 |
| 0.0776 | 2.31 | 60000 | 0.1224 | 0.1115 |
| 0.0756 | 2.33 | 60500 | 0.1071 | 0.1085 |
| 0.0753 | 2.35 | 61000 | 0.1072 | 0.1089 |
| 0.0698 | 2.37 | 61500 | 0.1129 | 0.1094 |
| 0.0726 | 2.38 | 62000 | 0.1109 | 0.1106 |
| 0.0758 | 2.4 | 62500 | 0.1052 | 0.1103 |
| 0.0743 | 2.42 | 63000 | 0.1079 | 0.1106 |
| 0.0765 | 2.44 | 63500 | 0.1248 | 0.1108 |
| 0.0724 | 2.46 | 64000 | 0.1248 | 0.1076 |
| 0.0659 | 2.48 | 64500 | 0.1099 | 0.1088 |
| 0.0674 | 2.5 | 65000 | 0.1156 | 0.1098 |
| 0.0691 | 2.52 | 65500 | 0.1122 | 0.1093 |
| 0.0677 | 2.54 | 66000 | 0.1228 | 0.1082 |
| 0.0695 | 2.56 | 66500 | 0.1049 | 0.1066 |
| 0.0687 | 2.58 | 67000 | 0.1025 | 0.1062 |
| 0.0682 | 2.6 | 67500 | 0.1080 | 0.1064 |
| 0.0663 | 2.61 | 68000 | 0.1009 | 0.1058 |
| 0.0654 | 2.63 | 68500 | 0.1145 | 0.1071 |
| 0.0641 | 2.65 | 69000 | 0.1178 | 0.1082 |
| 0.0662 | 2.67 | 69500 | 0.1106 | 0.1084 |
| 0.0623 | 2.69 | 70000 | 0.1086 | 0.1057 |
| 0.0692 | 2.71 | 70500 | 0.1048 | 0.1071 |
| 0.0663 | 2.73 | 71000 | 0.1119 | 0.1069 |
| 0.0639 | 2.75 | 71500 | 0.1147 | 0.1062 |
| 0.0597 | 2.77 | 72000 | 0.1121 | 0.1072 |
| 0.0688 | 2.79 | 72500 | 0.1149 | 0.1060 |
| 0.0616 | 2.81 | 73000 | 0.1126 | 0.1069 |
| 0.0633 | 2.83 | 73500 | 0.1302 | 0.1074 |
| 0.0651 | 2.85 | 74000 | 0.1260 | 0.1066 |
| 0.0637 | 2.86 | 74500 | 0.1233 | 0.1075 |
| 0.0641 | 2.88 | 75000 | 0.1199 | 0.1066 |
| 0.0655 | 2.9 | 75500 | 0.1249 | 0.1075 |
| 0.065 | 2.92 | 76000 | 0.1192 | 0.1061 |
| 0.0626 | 2.94 | 76500 | 0.1267 | 0.1069 |
| 0.0622 | 2.96 | 77000 | 0.1289 | 0.1094 |
| 0.0608 | 2.98 | 77500 | 0.1502 | 0.1096 |
| 0.0631 | 3.0 | 78000 | 0.1493 | 0.1099 |
| 0.0535 | 3.02 | 78500 | 0.1220 | 0.1064 |
| 0.0582 | 3.04 | 79000 | 0.1274 | 0.1077 |
| 0.052 | 3.06 | 79500 | 0.1296 | 0.1072 |
| 0.0562 | 3.08 | 80000 | 0.1160 | 0.1050 |
| 0.0533 | 3.1 | 80500 | 0.1066 | 0.1031 |
| 0.0564 | 3.11 | 81000 | 0.1300 | 0.1078 |
| 0.0589 | 3.13 | 81500 | 0.1167 | 0.1056 |
| 0.0582 | 3.15 | 82000 | 0.1129 | 0.1025 |
| 0.0594 | 3.17 | 82500 | 0.1255 | 0.1054 |
| 0.0559 | 3.19 | 83000 | 0.1258 | 0.1045 |
| 0.0535 | 3.21 | 83500 | 0.1150 | 0.1029 |
| 0.0538 | 3.23 | 84000 | 0.1043 | 0.1017 |
| 0.0537 | 3.25 | 84500 | 0.1073 | 0.1028 |
| 0.0534 | 3.27 | 85000 | 0.1011 | 0.1011 |
| 0.0527 | 3.29 | 85500 | 0.0987 | 0.1010 |
| 0.0549 | 3.31 | 86000 | 0.1008 | 0.1015 |
| 0.0516 | 3.33 | 86500 | 0.1031 | 0.1017 |
| 0.0549 | 3.35 | 87000 | 0.1103 | 0.1028 |
| 0.056 | 3.36 | 87500 | 0.0980 | 0.1008 |
| 0.0528 | 3.38 | 88000 | 0.1045 | 0.1020 |
| 0.0555 | 3.4 | 88500 | 0.0979 | 0.1005 |
| 0.0517 | 3.42 | 89000 | 0.0948 | 0.0992 |
| 0.0495 | 3.44 | 89500 | 0.0974 | 0.1002 |
| 0.0496 | 3.46 | 90000 | 0.1035 | 0.1013 |
| 0.0497 | 3.48 | 90500 | 0.1167 | 0.1035 |
| 0.0485 | 3.5 | 91000 | 0.1098 | 0.1009 |
| 0.0465 | 3.52 | 91500 | 0.1168 | 0.1009 |
| 0.05 | 3.54 | 92000 | 0.1088 | 0.1005 |
| 0.0514 | 3.56 | 92500 | 0.1116 | 0.1000 |
| 0.0467 | 3.58 | 93000 | 0.1053 | 0.0998 |
| 0.045 | 3.6 | 93500 | 0.1099 | 0.1012 |
| 0.0507 | 3.61 | 94000 | 0.1186 | 0.1012 |
| 0.0452 | 3.63 | 94500 | 0.1119 | 0.0998 |
| 0.0452 | 3.65 | 95000 | 0.1099 | 0.1002 |
| 0.0452 | 3.67 | 95500 | 0.1228 | 0.1015 |
| 0.0448 | 3.69 | 96000 | 0.1271 | 0.1025 |
| 0.0485 | 3.71 | 96500 | 0.1338 | 0.1037 |
| 0.048 | 3.73 | 97000 | 0.1288 | 0.1030 |
| 0.0476 | 3.75 | 97500 | 0.1183 | 0.1012 |
| 0.0457 | 3.77 | 98000 | 0.1171 | 0.1007 |
| 0.0492 | 3.79 | 98500 | 0.1142 | 0.1004 |
| 0.049 | 3.81 | 99000 | 0.1141 | 0.1006 |
| 0.046 | 3.83 | 99500 | 0.1165 | 0.1007 |
| 0.0444 | 3.85 | 100000 | 0.1173 | 0.1010 |
| 0.0456 | 3.86 | 100500 | 0.1150 | 0.1004 |
| 0.0467 | 3.88 | 101000 | 0.1130 | 0.1003 |
| 0.0465 | 3.9 | 101500 | 0.1137 | 0.1003 |
| 0.0451 | 3.92 | 102000 | 0.1127 | 0.1004 |
| 0.0445 | 3.94 | 102500 | 0.1118 | 0.1003 |
| 0.0453 | 3.96 | 103000 | 0.1112 | 0.1002 |
| 0.0458 | 3.98 | 103500 | 0.1103 | 0.1002 |
| 0.0454 | 4.0 | 104000 | 0.1101 | 0.1002 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.13.3
- Tokenizers 0.10.3
|
CAUKiel/JavaBERT-uncased | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"task_specific_params": {
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}
} | 7 | 2023-01-24T11:39:40Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: newwater/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAUKiel/JavaBERT | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 388 | 2023-01-24T11:44:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9175
- name: F1
type: f1
value: 0.917868093658934
---
<!-- 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/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2300
- Accuracy: 0.9175
- F1: 0.9179
## 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:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8387 | 1.0 | 250 | 0.3276 | 0.9045 | 0.9016 |
| 0.2573 | 2.0 | 500 | 0.2300 | 0.9175 | 0.9179 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CBreit00/DialoGPT_small_Rick | []
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/btc-doveywan-eth/1674562085261/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/1616618733556101124/oXxgxm8O_400x400.jpg')">
</div>
<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/1292159368943693824/JXYCQur0_400x400.jpg')">
</div>
<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/1258321209730760705/1hkrHoOT_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dovey "Rug The Fiat" Wan & BTC Times & ETH Zürich</div>
<div style="text-align: center; font-size: 14px;">@btc-doveywan-eth</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dovey "Rug The Fiat" Wan & BTC Times & ETH Zürich.
| Data | Dovey "Rug The Fiat" Wan | BTC Times | ETH Zürich |
| --- | --- | --- | --- |
| Tweets downloaded | 3244 | 3241 | 3246 |
| Retweets | 311 | 1215 | 1023 |
| Short tweets | 264 | 35 | 34 |
| Tweets kept | 2669 | 1991 | 2189 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fjov15tq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btc-doveywan-eth's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/n69s58ct) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/n69s58ct/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btc-doveywan-eth')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CL/safe-math-bot | []
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} | 0 | 2023-01-24T11:45:57Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 48.00 +/- 33.10
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CLAck/en-km | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
]
| translation | {
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} | 12 | null | Access to model jinwoo1126/tmp is restricted and you are not in the authorized list. Visit https://huggingface.co/jinwoo1126/tmp to ask for access. |
CLAck/en-vi | [
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 8 | null | ---
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: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 569.50 +/- 94.77
name: mean_reward
verified: false
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga stevaras2 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga stevaras2 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga stevaras2
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
CLTL/MedRoBERTa.nl | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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}
} | 2,988 | 2023-01-24T12:03:12Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AmirMesbah/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CLTL/icf-domains | [
"pytorch",
"roberta",
"nl",
"transformers",
"license:mit",
"text-classification"
]
| text-classification | {
"architectures": [
"RobertaForMultiLabelSequenceClassification"
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} | 35 | null | Few prompts:
1. palette knife painting of park with street lights in the style of smangrul
2. citylights painting in the style of smangrul
3. waves hitting the shore painting in the style of smangrul
4. a painting of a dream in the style of smangrul
5. night painting in the style of smangrul
Ouputs:
.png)
.png)
.png)
.png)
|
CLTL/icf-levels-enr | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
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}
} | 30 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: number_1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 269.50 +/- 10.95
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CLTL/icf-levels-etn | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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} | 31 | null | ---
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: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 597.00 +/- 225.89
name: mean_reward
verified: false
---
# **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 framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tim-binding -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tim-binding -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tim-binding
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
CLTL/icf-levels-ins | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 32 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/4878/michihasu-model |
CLTL/icf-levels-stm | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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},
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}
}
} | 32 | 2023-01-24T12:27:06Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: fuatuzumcu
---
### fuatuzumcu Dreambooth model trained by stablemobile with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
fuatuzumcu (use that on your prompt)
|
CM-CA/Cartman | []
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1108.72 +/- 76.49
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CM-CA/DialoGPT-small-cartman | []
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}
} | 0 | null | ---
license: gpl-3.0
datasets:
- multi_woz_v22
language:
- en
metrics:
- bleu
- rouge
---
Pretrained model: [GODEL-v1_1-base-seq2seq](https://huggingface.co/microsoft/GODEL-v1_1-base-seq2seq/)
Fine-tuning dataset: [MultiWOZ 2.2](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2)
# How to use:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gonced8/godel-multiwoz")
model = AutoModelForSeq2SeqLM.from_pretrained("gonced8/godel-multiwoz")
# Encoder input
context = [
"USER: I need train reservations from norwich to cambridge",
"SYSTEM: I have 133 trains matching your request. Is there a specific day and time you would like to travel?",
"USER: I'd like to leave on Monday and arrive by 18:00.",
]
input_text = " EOS ".join(context[-5:]) + " => "
model_inputs = tokenizer(
input_text, max_length=512, truncation=True, return_tensors="pt"
)["input_ids"]
# Decoder input
answer_start = "SYSTEM: "
decoder_input_ids = tokenizer(
"<pad>" + answer_start,
max_length=256,
truncation=True,
add_special_tokens=False,
return_tensors="pt",
)["input_ids"]
# Generate
output = model.generate(
model_inputs, decoder_input_ids=decoder_input_ids, max_length=256
)
output = tokenizer.decode(
output[0], clean_up_tokenization_spaces=True, skip_special_tokens=True
)
print(output)
# SYSTEM: TR4634 arrives at 17:35. Would you like me to book that for you?
``` |
CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 28 | null | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
CNT-UPenn/RoBERTa_for_seizureFrequency_QA | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 5 | null | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# AIAnGenV1
|
CSZay/bart | []
| null | {
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} | 0 | null | Access to model DhruvShek/Webraft-Ai is restricted and you are not in the authorized list. Visit https://huggingface.co/DhruvShek/Webraft-Ai to ask for access. |
CZWin32768/xlm-align | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2106.06381",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
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}
} | 6 | 2023-01-24T12:43:35Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.87 +/- 0.22
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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 import load_from_hub
...
```
|
Caddy/UD | []
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}
} | 0 | 2023-01-24T12:44:07Z | ---
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: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Schwarzschild009/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Calamarii/calamari | []
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} | 0 | null | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| summarization | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 145 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/btc-eth-vitalikbuterin/1674564747266/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<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/977496875887558661/L86xyLF4_400x400.jpg')">
</div>
<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/1292159368943693824/JXYCQur0_400x400.jpg')">
</div>
<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/1258321209730760705/1hkrHoOT_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">vitalik.eth & BTC Times & ETH Zürich</div>
<div style="text-align: center; font-size: 14px;">@btc-eth-vitalikbuterin</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from vitalik.eth & BTC Times & ETH Zürich.
| Data | vitalik.eth | BTC Times | ETH Zürich |
| --- | --- | --- | --- |
| Tweets downloaded | 3243 | 3241 | 3246 |
| Retweets | 241 | 1215 | 1023 |
| Short tweets | 123 | 35 | 34 |
| Tweets kept | 2879 | 1991 | 2189 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/d3n8pkg2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btc-eth-vitalikbuterin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x6co1yfz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x6co1yfz/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btc-eth-vitalikbuterin')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 35 | null | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 31 | null | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
Cameron/BERT-SBIC-targetcategory | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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"BertForSequenceClassification"
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} | 30 | null | ---
license: gpl-3.0
---
<img src="https://i.imgur.com/z2ODdOr.jpg" alt="drawing" style="width:300px;"/>
# About model
A simple model trained on a custom dataset containing over 100 coloring book type images.
If you enjoy this model and would like me to improve on it, [buy me a coffe](https://www.buymeacoffee.com/mrhup) ☕
# Installation:
Download both the ckpt and yaml files. Ensure that the same naming pattern is used and copy them under models/Stable-Diffusion path in your local/cloud SD installation. Stable Diffusion 2.1 is required for the model to work correctly.
# Black images issue:
2.1 models need to have a web-ui config modified - if you are getting black images - go to your config file and add to COMMANDLINE_ARGS= --no-half - potentially it could work with --xformers instead (if supported). This line might slow your generations a bit but will not affect negatively your output.
# Prompt suggestion:
`bichon havanese wearing sunglasses COLR_001, (((white background))), coloring book, line art, high resolution, black and white, colorless`
Negative: `((watermark)), (text), color, shading, gradient, shadows, transparency, noisy, blurred`
<img src="https://i.imgur.com/3iDf43z.png" alt="drawing" style="width:300px;"/><img src="https://i.imgur.com/TwVxNe1.jpg" alt="drawing" style="width:300px;"/>
<img src="https://i.imgur.com/vKrsyGe.jpg" alt="drawing" style="width:300px;"/><img src="https://i.imgur.com/Mp3vO5i.jpg" alt="drawing" style="width:300px;"/> |
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 36 | null | 2/06 いい感じになったTOTFN-5-25ができました
呼び出しは変わらず、補強はnavelが推奨 正則化画像の偏りか髪がグレーになる副作用あり
以前の話
呼び出しは“trick or treatment”のつもりです
これで補強したほうがいいかもしれないです“bikini,boot,gloves, layered bikini,purple bikini,pencil skirt,”
あんまり把握してないけどさすがLora、いい感じに見えるので
|
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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}
} | 37 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopterV2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 90.30 +/- 78.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 38 | null | I used the prompts and adopted them from [50 Stable Diffusion Photorealistic Portrait Prompts](https://decentralizedcreator.com/stable-diffusion-photorealistic-portrait-prompts/)
Few examples:
1. smangrul, hyperrealistic portrait, bladerunner street, art of elysium by jeremy mann and alphonse mucha, fantasy art, photo realistic, dynamic lighting, artstation, poster, volumetric lighting, very detailed face, 4 k, award winning
2. A potrait of handsome smangrul in studio ghibli style animation, 4k HD, busy tokyo city in the background
3. portrait of smangrul by WLOP
4. a potrait of handsome smangrul
5. face protrait of smangrul, jeremy mann painting
Ouputs:
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
|
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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} | 30 | null | ---
language:
- gos
---
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
More information on [GitHub](https://github.com/Bartelds/asr-augmentation). |
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 13 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1783.72 +/- 81.59
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Canadiancaleb/jessebot | []
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}
} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.90 +/- 12.01
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Canyonevo/DialoGPT-medium-KingHenry | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.90 +/- 0.67
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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 import load_from_hub
...
```
|
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab | []
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} | 0 | 2023-01-24T13:30:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9485085819030161
- name: Recall
type: recall
value: 0.9579266240323123
- name: F1
type: f1
value: 0.9531943397806245
- name: Accuracy
type: accuracy
value: 0.9919979751567306
---
<!-- 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 conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0176
- Precision: 0.9485
- Recall: 0.9579
- F1: 0.9532
- Accuracy: 0.9920
## 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:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.003 | 1.0 | 1756 | 0.0180 | 0.9397 | 0.9461 | 0.9429 | 0.9908 |
| 0.0013 | 2.0 | 3512 | 0.0163 | 0.9456 | 0.9566 | 0.9511 | 0.9919 |
| 0.0006 | 3.0 | 5268 | 0.0176 | 0.9485 | 0.9579 | 0.9532 | 0.9920 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1
- Datasets 2.7.1
- Tokenizers 0.13.1
|
Capreolus/birch-bert-large-car_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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} | 4 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: wooihen/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
]
| null | {
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}
} | 1 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 28 | null | ---
license: apache-2.0
language:
- en
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
pipeline_tag: automatic-speech-recognition
---
A fork of https://huggingface.co/openai/whisper-tiny.en exported to ONNX using [Optimum ONNX export](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model) 🤗
|
CarlosTron/Yo | []
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} | 0 | null | I used the prompts and adopted them from [50 Stable Diffusion Photorealistic Portrait Prompts](https://decentralizedcreator.com/stable-diffusion-photorealistic-portrait-prompts/):
1. A hyperrealistic painting of erenyeager, fantasy art, photo realistic, dynamic lighting, artstation, poster, volumetric lighting, very detailed face, 4 k, award winning
2. erenyeager having tea in a cafe facing eiffel tower
3. erenyeager in times square in the night, hyperrealistic, 4K, HD
Ouputs:
.png)
.png)
.png)
.png)
.png)
.png)
.png) |
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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"RobertaForSequenceClassification"
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} | 27 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: khaled5321/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/albert-large-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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"AlbertForTokenClassification"
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.78 +/- 0.16
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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 import load_from_hub
...
```
|
dccuchile/albert-large-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 5 | null | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.72 +/- 0.31
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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 import load_from_hub
...
```
|
dccuchile/albert-tiny-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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}
} | 29 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-fintuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8647266113447767
---
<!-- 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-fintuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1357
- F1: 0.8647
## 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:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2577 | 1.0 | 525 | 0.1719 | 0.8077 |
| 0.1254 | 2.0 | 1050 | 0.1362 | 0.8558 |
| 0.081 | 3.0 | 1575 | 0.1357 | 0.8647 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-tiny-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
tags:
- generated_from_trainer
model-index:
- name: russian-spellchecking2
results: []
---
<!-- 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. -->
# russian-spellchecking2
This model is a fine-tuned version of [UrukHan/t5-russian-spell](https://huggingface.co/UrukHan/t5-russian-spell) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
dccuchile/albert-tiny-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 31 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: khaled5321/RND-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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"AlbertForSequenceClassification"
],
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} | 26 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of sloth animal in the Acropolis
---
# DreamBooth model for the sloth concept trained by dobis-lks on the dobis-lks/test dataset.
This is a Stable Diffusion model fine-tuned on the sloth concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of sloth animal**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `animal` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('dobis-lks/sloth-animal')
image = pipeline().images[0]
image
```
|
dccuchile/albert-xlarge-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 24 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xlarge-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 7 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xlarge-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 29 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | {
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} | 26 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
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]
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} | 28 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 26 | 2023-01-24T14:41:52Z | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish-finetuned-pos | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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],
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} | 7 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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}
} | 68 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-base-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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}
} | 586 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-large-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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}
} | 75 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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} | 393 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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}
} | 91 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/albert-xxlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
| null | {
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}
} | 42 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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}
}
} | 27 | null | Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
|
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