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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pittawat/q-FrozenLake-v1-4x4-noSlippery
|
pittawat
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 397 |
# **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="pittawat/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"])
```
|
pittawat/q-Taxi-v3-eval
|
pittawat
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 369 |
# **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="pittawat/q-Taxi-v3-eval", 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"])
```
|
pittawat/q-Taxi-v3-eval-v2
|
pittawat
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Taxi-v3-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 372 |
# **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="pittawat/q-Taxi-v3-eval-v2", 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"])
```
|
kbleejohn/xlm-roberta-base-finetuned-panx-de
|
kbleejohn
|
xlm-roberta
| 18 | 0 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['xtreme']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,319 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1379
- F1: 0.8653
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2557 | 1.0 | 525 | 0.1583 | 0.8231 |
| 0.1269 | 2.0 | 1050 | 0.1393 | 0.8524 |
| 0.0826 | 3.0 | 1575 | 0.1379 | 0.8653 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ben-yu/ppo-Pyramids_v1
|
ben-yu
| null | 16 | 1 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']
| false | true | true | 832 |
# **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: ben-yu/ppo-Pyramids_v1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
UCSD-VA-health/RadBERT-2m
|
UCSD-VA-health
|
bert
| 8 | 5 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
| null | null | null | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
[]
| false | true | true | 2,103 |
## RadBERT-2m
This is a base model of Radiology-BERT from UC San Diego and VA healthcare system. It is initialized from BERT-base-uncased and further trained with 2 million radiology reports deidentified from US VA hospital. The model achieves stronger medical language understanding performance than previous medical domain models such as BioBERT, Clinical-BERT, BLUE-BERT and BioMed-RoBERTa.
Performances are evaluated on three tasks:
(a) abnormal sentence classification: sentence classification in radiology reports as reporting abnormal or normal findings;
(b) report coding: Assign a diagnostic code to a given radiology report for five different coding systems;
(c) report summarization: given the findings section of a radiology report, extractively select key sentences that summarized the findings.
It also shows superior performance on other radiology NLP tasks which are not reported in the paper.
For details, check out the paper here:
[RadBERT: Adapting transformer-based language models to radiology](https://pubs.rsna.org/doi/abs/10.1148/ryai.210258)
### How to use
Here is an example of how to use this model to extract the features of a given text in PyTorch:
```python
from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained('zzxslp/RadBERT-RoBERTa-4m')
tokenizer = AutoTokenizer.from_pretrained('zzxslp/RadBERT-RoBERTa-4m')
model = AutoModel.from_pretrained('zzxslp/RadBERT-RoBERTa-4m', config=config)
text = "Replace me by any medical text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### BibTeX entry and citation info
If you use the model, please cite our paper:
```bibtex
@article{yan2022radbert,
title={RadBERT: Adapting transformer-based language models to radiology},
author={Yan, An and McAuley, Julian and Lu, Xing and Du, Jiang and Chang, Eric Y and Gentili, Amilcare and Hsu, Chun-Nan},
journal={Radiology: Artificial Intelligence},
volume={4},
number={4},
pages={e210258},
year={2022},
publisher={Radiological Society of North America}
}
```
|
underactuated/opt-350m_rl1_v4
|
underactuated
|
opt
| 12 | 6 |
transformers
| 0 |
text-generation
| true | false | false |
other
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 911 |
<!-- 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. -->
# opt-350m_rl1_v4
This model is a fine-tuned version of [underactuated/opt-350m_mle_v3](https://huggingface.co/underactuated/opt-350m_mle_v3) 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: 3.0
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_128
|
gokuls
|
mobilebert
| 17 | 0 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,861 |
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4833
- Accuracy: 0.8544
## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5233 | 1.0 | 8748 | 0.5730 | 0.8291 |
| 0.3614 | 2.0 | 17496 | 0.5357 | 0.8394 |
| 0.3019 | 3.0 | 26244 | 0.5166 | 0.8509 |
| 0.268 | 4.0 | 34992 | 0.5172 | 0.8509 |
| 0.2465 | 5.0 | 43740 | 0.4833 | 0.8544 |
| 0.2313 | 6.0 | 52488 | 0.5422 | 0.8463 |
| 0.2201 | 7.0 | 61236 | 0.5778 | 0.8303 |
| 0.2113 | 8.0 | 69984 | 0.5364 | 0.8417 |
| 0.204 | 9.0 | 78732 | 0.5428 | 0.8314 |
| 0.198 | 10.0 | 87480 | 0.5442 | 0.8337 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
kruthof/climateattention-10k-upscaled
|
kruthof
|
roberta
| 7 | 9 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,641 |
# Description:
climateattention-10k classifies if a given sequence is related to climate topics.
As a fine-tuned classifier based on climatebert/distilroberta-base-climate-f (Webersinke et al., 2021),
it is using the following ClimaText dataset (Varini et al., 2020):
* AL-10Ks.tsv : 3000 (58 positives, 2942 negatives)
Due to the unbalanced character of the dataset, upscaling has been conducted before training.
# How to use:
```python
from transformers import AutoTokenizer, pipeline,RobertaForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("climatebert/distilroberta-base-climate-f")
climateattention = RobertaForSequenceClassification.from_pretrained('kruthof/climateattention-10k-upscaled',num_labels=2)
ClimateAttention = pipeline("text-classification", model=climateattention, tokenizer=tokenizer)
ClimateAttention('Emissions have increased during the last several months')
>> [{'label': 'Yes', 'score': 0.9993829727172852}]
```
# Performance:
Performance tested on the balanced ClimaText 10K test set, featuring 300 samples (67 positives, 233 negatives) (Varini et al., 2020)
|Accuracy| Precision | Recall | F1 |
|----|-----|-----|-----|
| 0.97 | 0.9531 | 0.9105 | 0.9313 |
# References:
Varini, F. S., Boyd-Graber, J., Ciaramita, M., & Leippold, M. (2020).
ClimaText: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483.
Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2021).
Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010.
------------------------------
https://kruthof.github.io
|
abhijit1247/female-it-engineer
|
abhijit1247
| null | 458 | 0 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']
| false | true | true | 384 |
# LoRA text2image fine-tuning - https://huggingface.co/abhijit1247/female-it-engineer
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the abhijit1247/female-it-engineer dataset. You can find some example images in the following.
|
Patters/q-FrozenLake-v1-4x4-noSlippery
|
Patters
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 396 |
# **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="Patters/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"])
```
|
Patters/taxi-v3
|
Patters
| null | 5 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 361 |
# **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="Patters/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"])
```
|
mallycrip/dqn-SpaceInvadersNoFrameskip-v4
|
mallycrip
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,221 |
# **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 mallycrip -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 mallycrip -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 mallycrip
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
tmobaggins/mbert-finetuned-squad
|
tmobaggins
|
bert
| 12 | 7 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 981 |
<!-- 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. -->
# mbert-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
cfalholt/Reinforce-CartPole-v1
|
cfalholt
| null | 6 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']
| true | true | true | 286 |
# **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
|
yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80
|
yujiepan
|
bert
| 12 | 59 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 872 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst2-unstructured-sparsity-80
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.
The sparsity on linear layers is 80%.
It achieves the following results on the evaluation set:
- eval_loss: 0.4133
- eval_accuracy: 0.9128
- eval_runtime: 31.5327
- eval_samples_per_second: 27.654
- eval_steps_per_second: 3.457
## Model description
- eval config： max_seq_length 128
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
huggingtweets/tomakado
|
huggingtweets
|
gpt2
| 11 | 0 |
transformers
| 0 |
text-generation
| true | false | false | null |
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['huggingtweets']
| false | true | true | 3,329 |
<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/1595550864986480647/-_fmzo0H_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">tomakado 🇺🇦</div>
<div style="text-align: center; font-size: 14px;">@tomakado</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 tomakado 🇺🇦.
| Data | tomakado 🇺🇦 |
| --- | --- |
| Tweets downloaded | 3130 |
| Retweets | 642 |
| Short tweets | 454 |
| Tweets kept | 2034 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/aw8qr930/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 @tomakado's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wjr5d06t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wjr5d06t/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/tomakado')
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)
|
kruthof/climateattention-10k
|
kruthof
|
roberta
| 7 | 18 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,659 |
# Description:
climateattention-10k classifies if a given sequence is related to climate topics.
As a fine-tuned classifier based on climatebert/distilroberta-base-climate-f (Webersinke et al., 2021),
it is using the following ClimaText dataset (Varini et al., 2020):
* AL-10Ks.tsv : 3000 (58 positives, 2942 negatives)
The training set is highly unbalanced. You might want to check the upscaling version: 'kruthof/climateattention-10k-upscaled'
# How to use:
```python
from transformers import AutoTokenizer, pipeline,RobertaForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("climatebert/distilroberta-base-climate-f")
climateattention = RobertaForSequenceClassification.from_pretrained('kruthof/climateattention-10k',num_labels=2)
ClimateAttention = pipeline("text-classification", model=climateattention, tokenizer=tokenizer)
ClimateAttention('Emissions have increased during the last several months')
>> [{'label': 'Yes', 'score': 0.9993829727172852}]
```
# Performance:
Performance tested on the balanced ClimaText 10K test set, featuring 300 samples (67 positives, 233 negatives) (Varini et al., 2020)
|Accuracy| Precision | Recall | F1 |
|----|-----|-----|-----|
| 0.9633 | 1 | 0.8358 | 0.9106 |
# References:
Varini, F. S., Boyd-Graber, J., Ciaramita, M., & Leippold, M. (2020).
ClimaText: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483.
Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2021).
Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010.
------------------------------
https://kruthof.github.io
|
sergioprada/clip-vit-base-patch322
|
sergioprada
|
clip
| 12 | 3 |
transformers
| 0 |
zero-shot-image-classification
| true | true | true | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['vision']
| false | true | true | 7,731 |
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
### Model Date
January 2021
### Model Type
The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
### Use with Transformers
```python3
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Performance and Limitations
### Performance
We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
- Food101
- CIFAR10
- CIFAR100
- Birdsnap
- SUN397
- Stanford Cars
- FGVC Aircraft
- VOC2007
- DTD
- Oxford-IIIT Pet dataset
- Caltech101
- Flowers102
- MNIST
- SVHN
- IIIT5K
- Hateful Memes
- SST-2
- UCF101
- Kinetics700
- Country211
- CLEVR Counting
- KITTI Distance
- STL-10
- RareAct
- Flickr30
- MSCOCO
- ImageNet
- ImageNet-A
- ImageNet-R
- ImageNet Sketch
- ObjectNet (ImageNet Overlap)
- Youtube-BB
- ImageNet-Vid
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
## Feedback
### Where to send questions or comments about the model
Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)
|
iZELX1/SlimeX
|
iZELX1
| null | 6 | 0 |
diffusers
| 2 |
text-to-image
| false | false | false |
creativeml-openrail-m
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['stable diffusion', 'stable diffusion diffusers', 'SlimeX']
| false | true | true | 2,449 |
### **SlimeX**
- # **[SlimeX](https://civitai.com/models/6963/slimex) by [Zanc](https://civitai.com/user/Zanc) (owner)**
**This model intends to produce high-quality, highly detailed anime style SFW and NSFW images.**
- **Slime** = No vae
- **SlimeX** = vae included
## Sample Images (SlimeX):
- # #1
```
masterpiece, best quality, 1girl, beautiful detailed eyes, perfect face, beautiful detailed face, looking at viewer, sigma 400mm f1.8, photo fine print, amazing sharp focus, ultra detailed, silver hair, upper body, navel, large breasts, race queen, black jacket, blue eyes, cat ears, long hair, sleepy, sweat, breathing, soft skin, indoors, afterglow
Negative prompt: (worst quality, low quality:1.4), (monochrome:1.3), (NSFW:1.4), 3d, text, frame, jpeg artifacts, grids, watermark, logo, username, text, flowers, particles, (missing fingers:1.3), bad hands,
Size: 448x576, Seed: 355678310, Model: SlimeX, Steps: 20, Sampler: DDIM, CFG scale: 8, Clip skip: 2, Model hash: f22782eb52, Hires steps: 20, Hires upscale: 1.85, Hires upscaler: Latent (nearest-exact), Denoising strength: 0.5
```
- # #2
```
masterpiece, best quality, izekonabe akio, 1girl
Negative prompt: (worst quality, low quality:1.4), (monochrome:1.4)
Size: 448x576, Seed: 3548745218, Model: SlimeX, Steps: 20, Sampler: DDIM, CFG scale: 8, Clip skip: 2, Model hash: f22782eb52, Hires steps: 20, Hires upscale: 1.85, Hires upscaler: Latent (nearest-exact), Denoising strength: 0.5
```
- # #3
```
masterpiece, best quality, ilyotaka haruhiko, solo, 1girl, solo, hair between eyes, long hair, short beard, light white purple hair, short hair, medium breasts, looking at viewer, thigh highs
Negative prompt: (worst quality, low quality:1.4), (monochrome:1.4)
Size: 448x576, Seed: 672966383, Model: SlimeX, Steps: 20, Sampler: DDIM, CFG scale: 8, Clip skip: 2, Model hash: f22782eb52, Hires steps: 20, Hires upscale: 1.85, Hires upscaler: Latent (nearest-exact), Denoising strength: 0.5
```
- # #4
## More sample images: [https://civitai.com/models/6963/slimex](https://civitai.com/models/6963/slimex)
|
morganjeffries/poca-SoccerTwos
|
morganjeffries
| null | 36 | 525 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 848 |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: morganjeffries/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
eshwarprasadS/lunarlanderV2-PPO
|
eshwarprasadS
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
PeterDerLustige/poca-SoccerTwos_V3
|
PeterDerLustige
| null | 20 | 524 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 852 |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: PeterDerLustige/poca-SoccerTwos_V3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
eldraco/poca-SoccerTwos-RoyKent-v3
|
eldraco
| null | 20 | 523 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 852 |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: eldraco/poca-SoccerTwos-RoyKent-v3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bingsu/dqn-SpaceInvadersNoFrameskip-v4
|
Bingsu
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,211 |
# **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 Bingsu -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 Bingsu -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 Bingsu
```
## 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)])
```
|
LowGI/STT_Model_5
|
LowGI
|
wav2vec2
| 12 | 4 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,001 |
<!-- 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. -->
# STT_Model_5
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1410
- Wer: 0.1808
## 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: 8
- 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: 1000
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.882 | 5.68 | 500 | 0.5476 | 0.9998 |
| 0.4219 | 11.36 | 1000 | 0.2672 | 0.7620 |
| 0.1972 | 17.05 | 1500 | 0.1670 | 0.4849 |
| 0.1313 | 22.73 | 2000 | 0.1425 | 0.3606 |
| 0.1002 | 28.41 | 2500 | 0.1259 | 0.2846 |
| 0.0752 | 34.09 | 3000 | 0.1307 | 0.2460 |
| 0.0552 | 39.77 | 3500 | 0.1193 | 0.2192 |
| 0.0426 | 45.45 | 4000 | 0.1264 | 0.2025 |
| 0.0319 | 51.14 | 4500 | 0.1237 | 0.1974 |
| 0.025 | 56.82 | 5000 | 0.1327 | 0.1904 |
| 0.0207 | 62.5 | 5500 | 0.1493 | 0.1921 |
| 0.0178 | 68.18 | 6000 | 0.1416 | 0.1865 |
| 0.0142 | 73.86 | 6500 | 0.1382 | 0.1802 |
| 0.0123 | 79.55 | 7000 | 0.1410 | 0.1808 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
relbert/relbert-roberta-large-iloob-a-semeval2012
|
relbert
|
roberta
| 30 | 6 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null |
['relbert/semeval2012_relational_similarity']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| true | true | true | 4,862 |
# relbert/relbert-roberta-large-iloob-a-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-a-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6684491978609626
- Accuracy on SAT: 0.6646884272997032
- Accuracy on BATS: 0.7559755419677598
- Accuracy on U2: 0.5921052631578947
- Accuracy on U4: 0.5856481481481481
- Accuracy on Google: 0.914
- Accuracy on ConceptNet Analogy: 0.46057046979865773
- Accuracy on T-Rex Analogy: 0.639344262295082
- Accuracy on NELL-ONE Analogy: 0.6183333333333333
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-a-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9180352568931747
- Micro F1 score on CogALexV: 0.8347417840375588
- Micro F1 score on EVALution: 0.661430119176598
- Micro F1 score on K&H+N: 0.9512415663907631
- Micro F1 score on ROOT09: 0.8965841429019116
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-a-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7736507936507937
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-iloob-a-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 10
- batch: 32
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: iloob
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-iloob-a-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
s3nh/SegFormer-b0-person-segmentation
|
s3nh
|
segformer
| 4 | 40 |
transformers
| 0 |
image-segmentation
| true | false | false |
openrail
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 390,110 |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
### Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
## Parameters
```
model = SegformerForSemanticSegmentation.from_pretrained("/notebooks/segformer_5_epoch",
num_labels=2,
id2label=id2label,
label2id=label2id, )
```
## Usage
```python
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
```
<img src = 'data:image/png;base64,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'>
|
pfunk/Pong-v4-DQPN_p2_pt0.1-seed1
|
pfunk
| null | 11 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 1,978 |
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p2_pt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p2_pt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p2_pt0.1 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_pt0.1-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_pt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p2_pt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p2_pt0.1 --start-policy-f 2000 --end-policy-f 2000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 2000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p2_pt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 0.1,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 2000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
qazisaad/ppo-LunarLander-v2
|
qazisaad
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
pfunk/Pong-v4-DQPN_p5_pt0.1_tt0.1-seed1
|
pfunk
| null | 11 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 2,026 |
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p5_pt0.1_tt0.1.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p5_pt0.1_tt0.1]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p5_pt0.1_tt0.1 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_pt0.1_tt0.1-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_pt0.1_tt0.1-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_pt0.1_tt0.1-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p5_pt0.1_tt0.1 --start-policy-f 5000 --end-policy-f 5000 --evaluation-fraction 1.00 --target-tau 0.1 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 5000,
'env_id': 'Pong-v4',
'evaluation_fraction': 1.0,
'exp_name': 'DQPN_p5_pt0.1_tt0.1',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 0.1,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 5000,
'target_network_frequency': 1000,
'target_tau': 0.1,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
relbert/relbert-roberta-large-iloob-b-semeval2012
|
relbert
|
roberta
| 30 | 8 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null |
['relbert/semeval2012_relational_similarity']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| true | true | true | 4,861 |
# relbert/relbert-roberta-large-iloob-b-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-b-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6283422459893048
- Accuracy on SAT: 0.6320474777448071
- Accuracy on BATS: 0.7854363535297387
- Accuracy on U2: 0.5570175438596491
- Accuracy on U4: 0.5787037037037037
- Accuracy on Google: 0.914
- Accuracy on ConceptNet Analogy: 0.3733221476510067
- Accuracy on T-Rex Analogy: 0.639344262295082
- Accuracy on NELL-ONE Analogy: 0.6866666666666666
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-b-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9046255838481242
- Micro F1 score on CogALexV: 0.8387323943661972
- Micro F1 score on EVALution: 0.6560130010834236
- Micro F1 score on K&H+N: 0.9515197885511582
- Micro F1 score on ROOT09: 0.8940770918207458
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-b-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.817202380952381
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-iloob-b-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 10
- batch: 32
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: iloob
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-iloob-b-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qnli_192
|
gokuls
|
distilbert
| 17 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,644 |
<!-- 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_sa_GLUE_Experiment_logit_kd_data_aug_qnli_192
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4463
- Accuracy: 0.5576
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.338 | 1.0 | 16604 | 0.4463 | 0.5576 |
| 0.2791 | 2.0 | 33208 | 0.4560 | 0.5711 |
| 0.256 | 3.0 | 49812 | 0.4603 | 0.5691 |
| 0.2446 | 4.0 | 66416 | 0.4620 | 0.5709 |
| 0.2379 | 5.0 | 83020 | 0.4547 | 0.5958 |
| 0.2334 | 6.0 | 99624 | 0.4581 | 0.5863 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
HasinMDG/SetFit_Labse_Sentiment_Towards_Topic_Arrow
|
HasinMDG
|
bert
| 15 | 59 |
sentence-transformers
| 0 |
sentence-similarity
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['sentence-transformers', 'feature-extraction', 'sentence-similarity']
| false | true | true | 2,271 |
# {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)
```
## 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 4655 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": 3,
"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": 13965,
"warmup_steps": 1397,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
thucdangvan020999/sd-class-butterflies-32
|
thucdangvan020999
| null | 6 | 2 |
diffusers
| 0 |
unconditional-image-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class']
| false | true | true | 374 |
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('thucdangvan020999/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
simplexico/project-filac-fact-issue
|
simplexico
|
mpnet
| 13 | 23 |
sentence-transformers
| 0 |
sentence-similarity
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['sentence-transformers', 'feature-extraction', 'sentence-similarity']
| false | true | true | 2,138 |
# {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)
```
## 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 762 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": 762,
"warmup_steps": 77,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Nicksinru/lunar
|
Nicksinru
| null | 12 | 5 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
pfunk/Pong-v4-DQPN_p5_e0.10-seed1
|
pfunk
| null | 11 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 1,979 |
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p5_e0.10.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p5_e0.10]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p5_e0.10 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_e0.10-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_e0.10-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_e0.10-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p5_e0.10 --start-policy-f 5000 --end-policy-f 1000 --evaluation-fraction 0.10 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 1000,
'env_id': 'Pong-v4',
'evaluation_fraction': 0.1,
'exp_name': 'DQPN_p5_e0.10',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 5000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
bimatechZou/Zouhaira_model
|
bimatechZou
|
distilbert
| 8 | 13 |
transformers
| 0 |
text-classification
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,788 |
<!-- 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. -->
# bimatechZou/Zouhaira_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Train Accuracy: 0.0
- Epoch: 7
## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.001, 'decay_steps': 1380, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| nan | nan | 0.0 | 0 |
| nan | nan | 0.0 | 1 |
| nan | nan | 0.0 | 2 |
| nan | nan | 0.0 | 3 |
| nan | nan | 0.0 | 4 |
| nan | nan | 0.0 | 5 |
| nan | nan | 0.0 | 6 |
| nan | nan | 0.0 | 7 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
relbert/relbert-roberta-large-iloob-c-semeval2012
|
relbert
|
roberta
| 30 | 6 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null |
['relbert/semeval2012_relational_similarity']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| true | true | true | 4,864 |
# relbert/relbert-roberta-large-iloob-c-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-c-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6737967914438503
- Accuracy on SAT: 0.6795252225519288
- Accuracy on BATS: 0.8187882156753752
- Accuracy on U2: 0.6096491228070176
- Accuracy on U4: 0.5879629629629629
- Accuracy on Google: 0.938
- Accuracy on ConceptNet Analogy: 0.43456375838926176
- Accuracy on T-Rex Analogy: 0.5683060109289617
- Accuracy on NELL-ONE Analogy: 0.6833333333333333
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-c-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9184872683441314
- Micro F1 score on CogALexV: 0.8765258215962441
- Micro F1 score on EVALution: 0.6852654387865655
- Micro F1 score on K&H+N: 0.9595882312026153
- Micro F1 score on ROOT09: 0.9122532121591977
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-c-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8343650793650794
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-iloob-c-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 10
- batch: 32
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: iloob
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-iloob-c-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
GesturingMan/dqn-SpaceInvadersNoFrameskip-v4
|
GesturingMan
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,229 |
# **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 GesturingMan -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 GesturingMan -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 GesturingMan
```
## 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)])
```
|
GoesUp/SloBertAA_Top5_WithOOC
|
GoesUp
|
camembert
| 11 | 45 |
transformers
| 0 |
text-classification
| true | false | false | null | null |
['GoesUp/RTVCommentsTop5UsersWithOOC']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 514 |
Model RTVCommentsTop5UsersWithOOC accepts a short text in Slovene language and returns an integer between 0 and 4, or OOC.
- If an integer beween 0 and 4 is returned, the model predicts that the given text was written by one of the top 5 most active
users on RTV SLO (ID 0 represents the user with the most comments written on the platform, while ID 4 represents the 5th most active user).
- If the result is OOC (*out-of-class*), the model predicted that the text was not written by any of those five users.
|
ottovoncwim/testpyramidsrnd
|
ottovoncwim
| null | 14 | 1 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']
| false | true | true | 837 |
# **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: ottovoncwim/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
helpingstar/ppo-Huggy
|
helpingstar
| null | 32 | 1 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']
| false | true | true | 822 |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** 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-Huggy
2. Step 1: Write your model_id: helpingstar/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
juusohugs/jack-of-many-trades
|
juusohugs
|
t5
| 8 | 21 |
transformers
| 0 |
text2text-generation
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 524 |
Asking questions
--
This model was fine-tuned for following tasks:
* "detect language"
* "list action points"
* "summarize"
* "ask a question"
* "sentiment analysis"
* "extract keywords"
* "identify problem"
* "identify opportunity"
* "recognize emotion"
* "classify text"
Each task needs to be written before the input text.
Example prompts:
--
* ask a question: that was some really good storytelling
* classify text: that was some really good storytelling
* extract keywords: that was some really good storytelling
|
eshwarprasadS/CarRacing_CNN_PPO
|
eshwarprasadS
| null | 19 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['CarRacing-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 346 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-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
...
```
|
xrenard/LunarLanderV2_PPO
|
xrenard
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
relbert/relbert-roberta-large-iloob-d-semeval2012
|
relbert
|
roberta
| 30 | 8 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null |
['relbert/semeval2012_relational_similarity']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| true | true | true | 4,861 |
# relbert/relbert-roberta-large-iloob-d-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-d-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6764705882352942
- Accuracy on SAT: 0.6735905044510386
- Accuracy on BATS: 0.7926625903279599
- Accuracy on U2: 0.6052631578947368
- Accuracy on U4: 0.6226851851851852
- Accuracy on Google: 0.968
- Accuracy on ConceptNet Analogy: 0.39093959731543626
- Accuracy on T-Rex Analogy: 0.6338797814207651
- Accuracy on NELL-ONE Analogy: 0.6416666666666667
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-d-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9171312339912611
- Micro F1 score on CogALexV: 0.8455399061032864
- Micro F1 score on EVALution: 0.6793066088840737
- Micro F1 score on K&H+N: 0.95875356472143
- Micro F1 score on ROOT09: 0.9072391099968662
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-d-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.844484126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-iloob-d-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 10
- batch: 32
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: iloob
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-iloob-d-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Erwanlbv/cnn-dqn-SpaceInvadersNoFrameskip-v4-n-10000
|
Erwanlbv
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,218 |
# **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 Erwanlbv -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 Erwanlbv -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 Erwanlbv
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
andreids/en_ner_sender_recipient
|
andreids
| null | 25 | 8 |
spacy
| 0 |
token-classification
| false | false | false | null |
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['spacy', 'token-classification']
| false | true | true | 704 |
| Feature | Description |
| --- | --- |
| **Name** | `en_ner_sender_recipient` |
| **Version** | `0.0.2` |
| **spaCy** | `>=3.4.3,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 514157 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (2 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `RECIPIENT`, `SENDER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 18.60 |
| `ENTS_P` | 35.08 |
| `ENTS_R` | 12.66 |
| `TOK2VEC_LOSS` | 385.52 |
| `NER_LOSS` | 4421.31 |
|
pfunk/Pong-v4-DQPN_p5_e0.25-seed1
|
pfunk
| null | 11 | 0 |
cleanrl
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']
| true | true | true | 1,980 |
# (CleanRL) **DQN** Agent Playing **Pong-v4**
This is a trained model of a DQN agent playing Pong-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p5_e0.25.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[DQPN_p5_e0.25]"
python -m cleanrl_utils.enjoy --exp-name DQPN_p5_e0.25 --env-id Pong-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_e0.25-seed1/raw/main/dqpn_atari.py
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_e0.25-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p5_e0.25-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqpn_atari.py --exp-name DQPN_p5_e0.25 --start-policy-f 5000 --end-policy-f 1000 --evaluation-fraction 0.25 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'end_policy_f': 1000,
'env_id': 'Pong-v4',
'evaluation_fraction': 0.25,
'exp_name': 'DQPN_p5_e0.25',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 80000,
'policy_tau': 1.0,
'save_model': True,
'seed': 1,
'start_e': 1,
'start_policy_f': 5000,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
iammartian0/cartv1
|
iammartian0
| null | 6 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']
| true | true | true | 286 |
# **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
|
emiltj/da_multi_dupli_onto_xlm_roberta_large
|
emiltj
| null | 16 | 3 |
spacy
| 0 |
token-classification
| false | false | false | null |
['da']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['spacy', 'token-classification']
| false | true | true | 868 |
| Feature | Description |
| --- | --- |
| **Name** | `da_multi_dupli_onto_xlm_roberta_large` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (16 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FACILITY`, `GPE`, `LAW`, `LOCATION`, `MONEY`, `NORP`, `ORDINAL`, `ORGANIZATION`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK OF ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 80.00 |
| `ENTS_P` | 77.85 |
| `ENTS_R` | 82.27 |
| `TRANSFORMER_LOSS` | 17711.38 |
| `NER_LOSS` | 289765.47 |
|
marmolpen3/all_MiniLM_L6_v2-sla
|
marmolpen3
|
bert
| 13 | 3 |
sentence-transformers
| 0 |
sentence-similarity
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['sentence-transformers', 'feature-extraction', 'sentence-similarity']
| false | true | true | 2,139 |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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)
```
## 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 770 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": 3,
"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": 2310,
"warmup_steps": 231,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
vimalgupta/layoutlmv3-finetuned-cord_vimal
|
vimalgupta
|
layoutlmv3
| 16 | 12 |
transformers
| 0 |
token-classification
| true | false | false |
cc-by-nc-sa-4.0
| null |
['cord-layoutlmv3']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,220 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-cord_vimal
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8321
- Precision: 0.7179
- Recall: 0.7368
- F1: 0.7273
- Accuracy: 0.7333
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 125.0 | 250 | 1.2027 | 0.7564 | 0.7763 | 0.7662 | 0.7481 |
| 0.8449 | 250.0 | 500 | 1.3990 | 0.7089 | 0.7368 | 0.7226 | 0.7333 |
| 0.8449 | 375.0 | 750 | 1.5343 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0296 | 500.0 | 1000 | 1.6144 | 0.75 | 0.75 | 0.75 | 0.7407 |
| 0.0296 | 625.0 | 1250 | 1.6898 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0134 | 750.0 | 1500 | 1.7402 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0134 | 875.0 | 1750 | 1.7888 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0089 | 1000.0 | 2000 | 1.8041 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0089 | 1125.0 | 2250 | 1.8209 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
| 0.0073 | 1250.0 | 2500 | 1.8321 | 0.7179 | 0.7368 | 0.7273 | 0.7333 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
NanditaP/Filter_Non-ExpertUser
|
NanditaP
|
bert
| 9 | 12 |
transformers
| 0 |
text-classification
| false | true | false |
cc-by-nc-nd-4.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,806 |
This model is a multi-class classifier, model fine-tuned using the model 'bert-base-uncased'.
It is built around a large corpus of Twitter users' metadata.
It filters the data into 3 main categories - (1) Non-ExpertUser (2) ExpertUser (3) Other.
The aim of this project was to find out whether a tweet belongs to an individual or not. And if it is, whether the person is an expert in the field of Security and Privacy.
Originally, the Model had 4 classes - where the 'Other' field was classified into 'Non-Person' (denoting accounts such as organizations)and 'Unknown'.
Since the main aim was to find out about whether a user is a non-expert user or not, the classes were reduced to 3 classes in this version 2.
The validation scores for the module were as follows
Accuracy = 0.93
<table>
<tr>
<th>Class</th>
<th>Precision</th>
<th>Recall</th>
<th>F1-Score</th>
</tr>
<tr>
<td>ExpertUser (0)</td>
<td>0.88</td>
<td>0.90</td>
<td>0.89</td>
</tr>
<tr>
<td><b>Non-ExpertUser (1)</b></td>
<td><b>0.95</b></td>
<td><b>0.97</b></td>
<td><b>0.96</b></td>
</tr>
<tr>
<td>Other (2)</td>
<td>0.85</td>
<td>0.78</td>
<td>0.81</td>
</tr>
</table>
<b>Paper:</b> The paper detailing how it was designed can be found here <a href="https://www.sciencedirect.com/science/article/pii/S016740482200400X">Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter</a>
<b>Please cite the paper if you use this model </b>:
Nandita Pattnaik, Shujun Li, and Jason R.C. Nurse. 2023.<br> Perspectives of non-expert users on cyber security and
privacy: An analysis of online discussions on Twitter.<br> Computers & Security 125 (2023), 103008. https://doi.org/10.1016/j.cose.2022.103008
|
yizhangliu/poca-SoccerTwos-v1
|
yizhangliu
| null | 20 | 442 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']
| false | true | true | 844 |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: yizhangliu/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
KoRiF/a2c-AntBulletEnv-v0
|
KoRiF
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 352 |
# **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
...
```
|
thucdangvan020999/ddpm-celebahq-finetuned-butterflies-2epochs
|
thucdangvan020999
| null | 6 | 0 |
diffusers
| 0 |
unconditional-image-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class']
| false | true | true | 355 |
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('thucdangvan020999/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
aidiary/ppo-LunarLander-v2
|
aidiary
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
fathyshalab/clinic-credit_cards-roberta
|
fathyshalab
|
roberta
| 14 | 4 |
sentence-transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['setfit', 'sentence-transformers', 'text-classification']
| false | true | true | 1,444 |
# fathyshalab/clinic-credit_cards-roberta
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("fathyshalab/clinic-credit_cards-roberta")
# 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}
}
```
|
relbert/relbert-roberta-large-iloob-e-semeval2012
|
relbert
|
roberta
| 30 | 6 |
transformers
| 0 |
feature-extraction
| true | false | false | null | null |
['relbert/semeval2012_relational_similarity']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| true | true | true | 4,863 |
# relbert/relbert-roberta-large-iloob-e-semeval2012
RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-e-semeval2012/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.6417112299465241
- Accuracy on SAT: 0.6320474777448071
- Accuracy on BATS: 0.7987770983879934
- Accuracy on U2: 0.6140350877192983
- Accuracy on U4: 0.5925925925925926
- Accuracy on Google: 0.938
- Accuracy on ConceptNet Analogy: 0.3775167785234899
- Accuracy on T-Rex Analogy: 0.5956284153005464
- Accuracy on NELL-ONE Analogy: 0.6583333333333333
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-e-semeval2012/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9219526894681332
- Micro F1 score on CogALexV: 0.8504694835680752
- Micro F1 score on EVALution: 0.6890574214517876
- Micro F1 score on K&H+N: 0.9642484523892328
- Micro F1 score on ROOT09: 0.8956439987464745
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-iloob-e-semeval2012/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8274206349206349
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-large-iloob-e-semeval2012")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-large
- max_length: 64
- epoch: 10
- batch: 32
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: iloob
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-iloob-e-semeval2012/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
manoharahuggingface/bert-finetuned-ner
|
manoharahuggingface
|
bert
| 12 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2003']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,518 |
<!-- 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: 0.0608
- Precision: 0.9362
- Recall: 0.9507
- F1: 0.9434
- Accuracy: 0.9866
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0869 | 1.0 | 1756 | 0.0684 | 0.9180 | 0.9369 | 0.9274 | 0.9823 |
| 0.033 | 2.0 | 3512 | 0.0681 | 0.9264 | 0.9487 | 0.9374 | 0.9854 |
| 0.0178 | 3.0 | 5268 | 0.0608 | 0.9362 | 0.9507 | 0.9434 | 0.9866 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Erwanlbv/cnn-dqn-SpaceInvadersNoFrameskip-v4-n-500000
|
Erwanlbv
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,218 |
# **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 Erwanlbv -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 Erwanlbv -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 Erwanlbv
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
zuzhe/Mecha-model
|
zuzhe
| null | 12 | 0 | null | 11 | null | false | false | false |
openrail
| null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 2,905 |
The mecha model needs low cfg, such as 3.5-7. Because the training set has only the upper body, it can only be partially stable,
Forgive me for not doing well,
Thanks to QQ friends for their long-term help and teaching. Thank you again
Thank Mr. Lin for his training set
BY昂扬
Use vae with high saturation
Real mechanical texture
Realistic
Metal details
Dirt, dust, damage and wear, battle damage
Mecha model
|
lora-library/wrestle
|
lora-library
| null | 3 | 0 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']
| false | true | true | 333 |
# LoRA DreamBooth - wrestle
These are LoRA adaption weights for [stabilityai/stable-diffusion-v1-5-base](https://huggingface.co/stabilityai/stable-diffusion-v1-5-base). The weights were trained on the instance prompt "erwestle" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
radedd/ppo-LunarLander-v2
|
radedd
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
jed351/gpt2_base_zh-hk-shikoto
|
jed351
|
gpt2
| 97 | 34 |
transformers
| 0 |
text-generation
| true | false | false |
openrail
| null |
['jed351/shikoto_zh_hk']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,253 |
# gpt2-shikoto
This model was trained on a dataset I obtained from an online novel site.
**Please be aware that the stories (training data) might contain inappropriate content. This model is intended for research purposes only.**
The base model can be found [here](https://huggingface.co/jed351/gpt2-base-zh-hk), which was obtained by
patching a [GPT2 Chinese model](https://huggingface.co/ckiplab/gpt2-base-chinese) and its tokenizer with Cantonese characters.
Refer to the base model for info on the patching process.
Besides language modeling, another aim of this experiment was to test the accelerate library by offloading certain workloads to CPU as well as finding the optimal training iterations.
The perplexity of this model is 16.12 after 400,000 steps. Comparing to the previous [attempt](https://huggingface.co/jed351/gpt2_tiny_zh-hk-shikoto) 27.02 after 400,000 steps.
It took around the same time duration to train this model but I only used 1 GPU here.
## Training procedure
Please refer to the [script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling)
provided by Huggingface.
The model was trained for 400,000 steps on 1 NVIDIA Quadro RTX6000 for around 30 hours at the Research Computing Services of Imperial College London.
### How to use it?
```
from transformers import AutoTokenizer
from transformers import TextGenerationPipeline, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2-base-zh-hk")
model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_base_zh-hk-shikoto")
# try messing around with the parameters
generator = TextGenerationPipeline(model, tokenizer,
max_new_tokens=200,
no_repeat_ngram_size=3) #, device=0) #if you have a GPU
input_string = "your input"
output = generator(input_string)
string = output[0]['generated_text'].replace(' ', '')
print(string)
```
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Ywl9909/finetuning-sentiment-model-3000-samples
|
Ywl9909
|
distilbert
| 13 | 0 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['imdb']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,053 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3142
- Accuracy: 0.88
- F1: 0.8824
## 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: 2
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
UchihaMadara/model1-thesis
|
UchihaMadara
|
bert
| 12 | 5 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,516 |
<!-- 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. -->
# model1-thesis
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7088
- Precision: 0.0487
- Recall: 0.0532
- F1: 0.0509
- Accuracy: 0.3175
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 12 | 1.7903 | 0.0152 | 0.0169 | 0.0160 | 0.2535 |
| No log | 2.0 | 24 | 1.7359 | 0.0458 | 0.0464 | 0.0461 | 0.2944 |
| No log | 3.0 | 36 | 1.7088 | 0.0487 | 0.0532 | 0.0509 | 0.3175 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
zuxi/Anterkiar
|
zuxi
| null | 20 | 0 |
diffusers
| 0 | null | false | false | false |
openrail
| null | null | null | 2 | 0 | 2 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 4,609 |
# Model Card for Model ID
这个模型是用来跑图的
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
这是一个一个一个融合模型
- ** Developedby:** yushui
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
放到webui里就能用
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
logoyazilim/crnn_vgg16_bn_20230206-155907
|
logoyazilim
| null | 4 | 4 |
transformers
| 0 | null | true | false | false | null |
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,612 |
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
### Run Configuration
{
"arch": "crnn_vgg16_bn",
"train_path": "doctr-train-40k",
"val_path": null,
"train_samples": 1000,
"val_samples": 20,
"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf",
"min_chars": 1,
"max_chars": 12,
"name": null,
"epochs": 10,
"batch_size": 32,
"device": 0,
"input_size": 32,
"lr": 0.001,
"weight_decay": 0,
"workers": 4,
"resume": null,
"vocab": "turkish",
"test_only": false,
"show_samples": false,
"wb": false,
"push_to_hub": true,
"pretrained": true,
"sched": "cosine",
"amp": false,
"find_lr": false
}
|
ireneisdoomed/stop_reasons_classificator_multilabel_pt
|
ireneisdoomed
|
bert
| 13 | 4 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,661 |
<!-- 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. -->
# stop_reasons_classificator_multilabel_pt
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0899
- Accuracy Thresh: 0.9760
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| No log | 1.0 | 106 | 0.1824 | 0.9475 |
| No log | 2.0 | 212 | 0.1339 | 0.9630 |
| No log | 3.0 | 318 | 0.1109 | 0.9689 |
| No log | 4.0 | 424 | 0.0988 | 0.9741 |
| 0.1439 | 5.0 | 530 | 0.0943 | 0.9743 |
| 0.1439 | 6.0 | 636 | 0.0891 | 0.9763 |
| 0.1439 | 7.0 | 742 | 0.0899 | 0.9760 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
juro95/xlm-roberta-finetuned-ner-cased_0.5_ratio
|
juro95
|
xlm-roberta
| 8 | 3 |
transformers
| 0 |
token-classification
| false | true | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,491 |
<!-- 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. -->
# juro95/xlm-roberta-finetuned-ner-cased_0.5_ratio
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0650
- Validation Loss: 0.1062
- Epoch: 3
## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 98380, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3015 | 0.1617 | 0 |
| 0.1437 | 0.1239 | 1 |
| 0.0939 | 0.1087 | 2 |
| 0.0650 | 0.1062 | 3 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.6.5
- Datasets 2.3.2
- Tokenizers 0.13.2
|
malibanekg/Reinforce-CartPole-v1
|
malibanekg
| null | 6 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']
| true | true | true | 286 |
# **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
|
BeardedJohn/bert-ner-wikiann
|
BeardedJohn
|
bert
| 8 | 36 |
transformers
| 0 |
token-classification
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,505 |
<!-- 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. -->
# bert-ner-wikiann
This model is a fine-tuned version of [BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2](https://huggingface.co/BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1926
- Validation Loss: 0.3131
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1875, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3988 | 0.3206 | 0 |
| 0.2620 | 0.2997 | 1 |
| 0.1926 | 0.3131 | 2 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
LowGI/STT_Model_7
|
LowGI
|
wav2vec2
| 14 | 1 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,462 |
<!-- 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. -->
# STT_Model_7
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6921
- Wer: 0.3465
## 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: 8
- 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: 1000
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 20.0 | 200 | 2.9402 | 1.0 |
| No log | 40.0 | 400 | 2.8466 | 1.0 |
| 3.4486 | 60.0 | 600 | 0.6408 | 0.4648 |
| 3.4486 | 80.0 | 800 | 0.5779 | 0.4141 |
| 0.2246 | 100.0 | 1000 | 0.6921 | 0.3465 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
cookpad/CookBERT-uncased-id_v2
|
cookpad
|
bert
| 9 | 14 |
transformers
| 0 |
fill-mask
| true | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | false | true | 217 |
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("cookpad/CookBERT-uncased-id_v2")
model = AutoModelForMaskedLM.from_pretrained("cookpad/CookBERT-uncased-id_v2")
|
aidiary/dqn-MountainCar-v0
|
aidiary
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['MountainCar-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
UchihaMadara/model1-thesis-2
|
UchihaMadara
|
bert
| 16 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,518 |
<!-- 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. -->
# model1-thesis-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2895
- Precision: 0.3090
- Recall: 0.3740
- F1: 0.3384
- Accuracy: 0.4889
## 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: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 12 | 1.4038 | 0.1997 | 0.1653 | 0.1809 | 0.4624 |
| No log | 2.0 | 24 | 1.3086 | 0.2769 | 0.2777 | 0.2773 | 0.4911 |
| No log | 3.0 | 36 | 1.2895 | 0.3090 | 0.3740 | 0.3384 | 0.4889 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Closen/ppo-SnowballTarget
|
Closen
| null | 6 | 0 |
ml-agents
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']
| false | true | true | 853 |
# **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: Closen/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mikegarts/a2c-AntBulletEnv-v0
|
mikegarts
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 352 |
# **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
...
```
|
figfig/whisper-small-en
|
figfig
|
whisper
| 26 | 55 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['en']
|
['figfig/restaurant_order_test']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['hf-asr-leaderboard', 'generated_from_trainer']
| true | true | true | 1,464 |
<!-- 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. -->
# restaurant_test_model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the test_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5435
- Wer: 78.5714
## 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: 5
- training_steps: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 10.0 | 10 | 2.2425 | 7.1429 |
| No log | 20.0 | 20 | 0.6651 | 0.0 |
| 2.4375 | 30.0 | 30 | 0.5776 | 35.7143 |
| 2.4375 | 40.0 | 40 | 0.5435 | 78.5714 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Phantom-Artist/phantom-diffusion-s2
|
Phantom-Artist
| null | 68 | 0 | null | 7 | null | false | false | false |
creativeml-openrail-m
|
['en', 'ja']
|
['Phantom-Artist/phantom-diffusion-s2-dataset']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['art']
| false | true | true | 7,379 |
Another 20 models that are trained over [hakurei's Waifu Diffusion](https://huggingface.co/hakurei/waifu-diffusion).
Each model was trained on a notable Japanese AI artist's works using dreambooth, with 30 of their works gained mainly from twitter (except for aibanousagi, which I could find only 23).
It tooks 1000 steps to train each model, at a learning rate of 5-e6. I have shared the notebook [here](https://huggingface.co/Phantom-Artist/phantom-diffusion/blob/main/DreamBooth_Stable_Diffusion_works_with_SD_2.ipynb) in case you want to try out additional training.
Some are good at backgrounds, while others can generate a semi-realistic style as seen in many SD 1.5 anime/waifu models (and some might be a bit overfitted).
This time, sevral of them are also quite good at generating a person with a certain fashion (just as the swingwings_style in the first 20).
The dataset is published [here](https://huggingface.co/datasets/Phantom-Artist/phantom-diffusion-s2-dataset).
# For those who are against generative AIs
You can see that now they are the target. Take our tool. Now is the time for pay back. Generate the images in their styles, and bring back the power you had to yourself.
# For those who support the development of generative AIs
Some of the AI artists, even though they take advantage of the open strategy of Stable Diffusion, now tend to hide their prompts, trying to monopolize their style (I'm not saying the AI artists I trained are as such, to be sure).
To continue protecting our values and beliefs on the open community and fight against them trying to create another pre-modern style guilds, I will show you a new way.
You no longer need their prompts; just train their images by yourself to protect the open community. It's not only legal but also ethical, as they have been taking advantages of others' trained dataset.
# For those who call themselves "phantom 20"
Note that these artists are IMO as interesting as you are.
Some of them were in the second because I had limited time before disclosing the first series, some are recommendation through a feedback, and others are those who I found out to be interesting enough to be on the list after having communication on the discord server.
ALL of them, however, are as interesting, with their specific styles, and I recommend you that you call yourselves "phantom *40*."
# Will I add more models?
**No**, at least, not with the speed I did in the first and the second series.
I think I have covered 90% of those who are notable and interesting, and I'm kind of tired to see people trying to be or pushing someone to be in the waiting list of the phantom series.
You can simply do it yourself. That's why I have that notebook left open.
# trained artist list
- ALyo
- cfromi_icwit
- recommended additional pos: photorealistic
- Defpoint
- getkomusen
- hemlok_ai
- Inakamono_AI
- kaideen_bird
- recommended additional neg: bird
- kenzenna_ifuru
- kuyoh
- kyo_do4
- recommended to remove neg: nsfw
- littoch
- recommended additional pos: crying/beautiful tears
- moto_ourt
- nakamurashippo
- recommended additional neg: wrong perspective, distorted buildings
- sai81685361
- recommended additional pos: loli, big round eyes, flat chest
- ShunkaCule
- The_Pioneer
- possible additional pos: CG
- waterman
- yamaneko_catfox
- possible additional pos: cat ears
- Yoshi_kake0712
- SA1P
# samples
The basic prompt is as follows.
However, to present you the potential of these models as much as possible, many of them have additional postive tags (such as "in the style of") to get the result below (yes, use ``aitop (ARTIST)_style`` to gain the finetuned result).
Many works better with the additional prompt ``beautiful face``. Generally speaking, prompting words close to the trained dataset will give you a better result.
```
POS: masterpiece, best quality, 1girl, aitop (ARTIST)_style
NEG: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digits, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, simple background
```
## ALyo
## cfromi_icwit
## Defpoint
## getkomusen
## hemlok_ai
## Inakamono_AI
## kaideen_bird
## kenzenna_ifuru
## kuyoh
## kyo_do4
## littoch
## moto_ourt
## nakamurashippo
## sai81685361
## ShunkaCule
## The_Pioneer
## waterman
## yamaneko_catfox
## Yoshi_kake0712
## SA1P
|
DaniilSirota/Reinforce_pixelcopter
|
DaniilSirota
| null | 6 | 0 | null | 1 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Pixelcopter-PLE-v0', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']
| true | true | true | 300 |
# **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
|
Seyfelislem/wspr-sm-ar
|
Seyfelislem
|
whisper
| 14 | 4 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,458 |
<!-- 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. -->
# wspr-sm-ar
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4515
- Wer: 72.6173
## 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: 16
- 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: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4569 | 0.25 | 500 | 0.8556 | 105.5427 |
| 0.5478 | 0.5 | 1000 | 0.7056 | 86.3373 |
| 0.2269 | 0.75 | 1500 | 0.6320 | 114.2627 |
| 0.1936 | 1.12 | 2000 | 0.4515 | 72.6173 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.11.0
- Datasets 2.9.1.dev0
- Tokenizers 0.12.1
|
LowGI/STT_Model_8
|
LowGI
|
wav2vec2
| 12 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,763 |
<!-- 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. -->
# STT_Model_8
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5858
- Wer: 0.3549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Dataset info
- Name: LJSpeech
- Source: https://www.kaggle.com/datasets/mathurinache/the-lj-speech-dataset
- Total audios (in Google Drive): 1420
- Total transcripts (in Google Drive): 13100
- No. of rows selected: 100
- Train-test ratio: 80:20
- No. of training set: 80
- No. of testing set: 20
## 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: 8
- 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: 1000
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 20.0 | 200 | 2.9443 | 1.0 |
| No log | 40.0 | 400 | 2.8603 | 1.0 |
| 3.8362 | 60.0 | 600 | 0.5940 | 0.4197 |
| 3.8362 | 80.0 | 800 | 0.5702 | 0.3380 |
| 0.2307 | 100.0 | 1000 | 0.5858 | 0.3549 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
sanchit-gandhi/ast-fleurs-langid-dropout-0.2
|
sanchit-gandhi
|
audio-spectrogram-transformer
| 9 | 2 |
transformers
| 0 |
audio-classification
| true | false | false |
bsd-3-clause
| null |
['fleurs']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,513 |
<!-- 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. -->
# ast-fleurs-langid-dropout-0.2
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 7.3600
- Accuracy: 0.1819
## 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: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0251 | 1.0 | 16987 | 6.7973 | 0.1689 |
| 0.0007 | 2.0 | 33974 | 7.3461 | 0.1787 |
| 0.0 | 3.0 | 50961 | 7.3600 | 0.1819 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
dasaprakashk/dqn-SpaceInvadersNoFrameskip-v4
|
dasaprakashk
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,229 |
# **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 dasaprakashk -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 dasaprakashk -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 dasaprakashk
```
## 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)])
```
|
Mykolyt/Reinforce-CartPole-v1
|
Mykolyt
| null | 6 | 0 | null | 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']
| true | true | true | 286 |
# **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
|
02shanky/finetuned-distilbert-base-uncased-emotion
|
02shanky
|
distilbert
| 13 | 4 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['emotion']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,060 |
<!-- 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. -->
# finetuned-distilbert-base-uncased-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: 0.1535
- Accuracy: 0.9395
- F1: 0.9396
## 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: 8
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
mikegarts/a2c-PandaReachDense-v2
|
mikegarts
| null | 13 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 358 |
# **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
...
```
|
JessicaHsu/ppo-LunarLander-v2
|
JessicaHsu
| null | 12 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 350 |
# **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
...
```
|
lyk0013/bert-finetuned-ner
|
lyk0013
|
bert
| 10 | 6 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['conll2003']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,426 |
<!-- 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: 0.1689
- Precision: 0.8091
- Recall: 0.8699
- F1: 0.8384
- Accuracy: 0.9526
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 100 | 0.1917 | 0.7899 | 0.8584 | 0.8227 | 0.9430 |
| No log | 2.0 | 200 | 0.1689 | 0.8091 | 0.8699 | 0.8384 | 0.9526 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Erwanlbv/cnn-ppo-SpaceInvadersNoFrameskip-v4
|
Erwanlbv
| null | 15 | 0 |
stable-baselines3
| 0 |
reinforcement-learning
| false | false | false | null | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']
| true | true | true | 2,086 |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** 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 ppo --env SpaceInvadersNoFrameskip-v4 -orga Erwanlbv -f logs/
python -m rl_zoo3.enjoy --algo ppo --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 ppo --env SpaceInvadersNoFrameskip-v4 -orga Erwanlbv -f logs/
python -m rl_zoo3.enjoy --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Erwanlbv
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
pavanBuduguppa/bert-base-cased-nsp-abcdv1.1
|
pavanBuduguppa
|
bert
| 8 | 5 |
transformers
| 0 | null | true | false | false |
gpl-2.0
|
['en']
|
['pavanBuduguppa/abcdv1.1_nsp']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['contactCenter', 'chat', 'digital']
| false | true | true | 4,906 |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
opentargets/clinical_trial_stop_reasons
|
opentargets
|
bert
| 12 | 9 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['opentargets/clinical_trial_reason_to_stop']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer', 'medical']
| true | true | true | 1,782 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Clinical trial stop reasons
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the task of classification of why a clinical trial has stopped early.
The dataset containing 3,747 manually curated reasons used for fine-tuning is available in the [Hub](https://huggingface.co/datasets/opentargets/clinical_trial_reason_to_stop).
More details on the model training are available in the GitHub project ([link](https://github.com/opentargets/stopReasons)) and in the associated publication (TBC).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| No log | 1.0 | 106 | 0.1824 | 0.9475 |
| No log | 2.0 | 212 | 0.1339 | 0.9630 |
| No log | 3.0 | 318 | 0.1109 | 0.9689 |
| No log | 4.0 | 424 | 0.0988 | 0.9741 |
| 0.1439 | 5.0 | 530 | 0.0943 | 0.9743 |
| 0.1439 | 6.0 | 636 | 0.0891 | 0.9763 |
| 0.1439 | 7.0 | 742 | 0.0899 | 0.9760 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
yunaaa/translated_model
|
yunaaa
|
t5
| 21 | 11 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-4.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 957 |
<!-- 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. -->
# translated_model
This model is a fine-tuned version of [paust/pko-t5-small](https://huggingface.co/paust/pko-t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- 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
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
KindLab/roberta-deid
|
KindLab
|
roberta
| 5 | 19 |
transformers
| 0 |
token-classification
| true | false | false | null |
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['medical']
| false | true | true | 487 |
Protected health information (PHI) anonymization tool. Fine-tuned on the [i2b2 2014 training dataset](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989908/) from the pretrained `roberta-base` model.
Anonymizes according to the i2b2 2014 standard, including all ages, locations and organizations, dates (including lone years), names, professions, identification numbers, and contact information.
Model released with the approval of Informatics for Integrating Biology & the Bedside.
|
BeardedJohn/bert-finetuned-ner-per-v5
|
BeardedJohn
|
bert
| 8 | 21 |
transformers
| 0 |
token-classification
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,367 |
<!-- 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. -->
# bert-finetuned-ner-per-v5
This model is a fine-tuned version of [BeardedJohn/bert-ner-wikiann](https://huggingface.co/BeardedJohn/bert-ner-wikiann) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0136
- Validation Loss: 0.0009
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 313, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0136 | 0.0009 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
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